Overall Recommendation & Rating
8
Excellent
Insta-Insights: Analyzing User Behavior on Instagram with SQL
Candidate Details
REPORT SUMMARY
Positive Feedback
  • Good understanding and implementation of task requirements.
  • Proficiency in Python and Pandas skills.
  • Efficient code with room for improvement in comments.
  • Clear understanding of data manipulation tasks.
  • Solid understanding of SQL queries and task requirements.
Scope of Improvement
  • Enhance code with detailed comments for data manipulation process
  • Explore advanced Pandas functions for complex data operations
  • Focus on code clarity and readability with descriptive comments
  • Optimize code performance and consider edge cases for error handling
  • Incorporate informative comments and insightful code structure
Performance Based Rating
9
Code Syntax
8
Code Clarity
7
Well Commented
9
Task Understanding
9
Performance Efficiency
Role and Skill Based Rating
9
MySql
9
Python
9
Pandas
9
Data Analyst
9
Pandas
9
SQL
Project Description

Instagram is one of the most popular social media platforms in the world, with millions of active users sharing photos, videos, and comments every day.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent, enhancing code readability.
  • Area of Improvement: Ensure consistent naming conventions and formatting throughout the code for better code maintenance.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and easy to follow. Variable names are descriptive and meaningful. The solution code correctly imports the 'comments.csv' file, removes unwanted columns, renames columns, and saves the cleaned DataFrame as 'comments_cleaned.csv'.
  • Area of Improvement: Consider adding more comments to explain the purpose of each step in the code. Ensure consistency in commenting style throughout the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more detailed comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain certain steps, enhancing readability. The comments provided are clear and helpful in understanding the code flow.
  • Area of Improvement: Add more comments to elaborate on the logic behind each operation and provide insights into the data manipulation process.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall commenting quality.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task description.
  • Area of Improvement: To further enhance task understanding, consider exploring additional Pandas functionalities for data manipulation and transformation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in exploring advanced Pandas features.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently achieves the task requirements with minimal complexity. It reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame effectively.
  • Area of Improvement: Optimize the code further by checking for any redundant operations or unnecessary steps that can be streamlined.
  • Final Verdict: The code demonstrates good performance efficiency in completing the task.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation tasks. The code showcases a good understanding of Python syntax and data handling.
  • Area of Improvement: To enhance Python skills, consider practicing more complex data analysis tasks and exploring advanced Pandas functionalities.
  • Final Verdict: The user exhibits strong Python skills with potential for further growth in advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame, showcasing proficiency in data manipulation using Pandas.
  • Area of Improvement: To further enhance Pandas skills, consider exploring more advanced data transformation techniques and handling larger datasets.
  • Final Verdict: The user demonstrates strong Pandas skills with potential for growth in handling more complex data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's code aligns well with the responsibilities of a Data Analyst by effectively cleaning and preparing data for analysis. The use of Pandas for data manipulation reflects skills relevant to a Data Analyst role.
  • Area of Improvement: To excel as a Data Analyst, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user exhibits capabilities aligned with a Data Analyst role, with opportunities for further development in advanced data analysis techniques.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code demonstrates correct syntax usage and adheres to Python coding standards. Variable naming conventions are appropriate, and there are no syntax errors present.
  • Area of Improvement: Ensure consistent spacing and indentation throughout the code for better readability and maintainability.
  • Final Verdict: The code maintains good syntax practices with minor areas for improvement in formatting.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. It successfully reads the 'follows.csv' file, removes the specified columns, renames the columns, and saves the cleaned DataFrame. The code structure is easy to follow.
  • Area of Improvement: To enhance code clarity, consider adding comments to explain each step of the process. Additionally, ensure consistent indentation throughout the code.
  • Final Verdict: Overall, the code demonstrates good clarity with room for improvement in commenting and code structure.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic behind each operation. However, due to the simplicity of the task, the code remains somewhat understandable even without extensive comments.
  • Area of Improvement: Adding comments to describe the purpose of each line of code would greatly enhance the readability and maintainability of the script.
  • Final Verdict: While the code is somewhat understandable without comments, adding descriptive comments would significantly improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has successfully completed the task by reading the 'follows.csv' file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task requirements.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced Pandas functionalities and error handling techniques.
  • Final Verdict: The user demonstrates a solid understanding of the task with opportunities for growth in exploring more complex Pandas operations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently removes the unwanted columns and renames the columns as required. It saves the cleaned DataFrame without any unnecessary operations, leading to a streamlined process.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the renaming process and exploring methods to enhance the overall execution speed.
  • Final Verdict: The code showcases good performance efficiency with opportunities for optimization in certain areas.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and file operations. The code showcases a good grasp of Python syntax and libraries.
  • Area of Improvement: To further enhance Python skills, consider exploring more advanced Pandas functionalities and incorporating error handling mechanisms.
  • Final Verdict: The user exhibits strong Python skills with potential for growth in more complex data processing tasks.
Pandas
  • Rating: 8.5
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code demonstrates a good understanding of Pandas operations for data manipulation.
  • Area of Improvement: To further enhance Pandas skills, explore more advanced functionalities and techniques within the Pandas library.
  • Final Verdict: The user displays strong Pandas skills with potential for growth in handling more complex data processing tasks.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with tasks typically performed by a Data Analyst, such as data cleaning and manipulation. The use of Pandas for data processing reflects skills relevant to the Data Analyst role.
  • Area of Improvement: To further develop Data Analyst skills, consider delving into statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user demonstrates proficiency in tasks associated with the Data Analyst role, with opportunities for growth in advanced data analysis techniques.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows Python standards, and is free from syntax errors.
  • Area of Improvement: To maintain code syntax consistency, the user should ensure proper spacing and indentation throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in spacing and indentation.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to understand. Variable names are meaningful, and the solution follows the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To enhance the quality of comments, the user should consider adding comments for each major step in the code, providing more insights into the logic.
  • Final Verdict: While some comments are present, more detailed explanations would improve the overall quality of comments.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task by successfully reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could focus on providing more detailed comments and exploring additional Pandas functionalities for data manipulation.
  • Final Verdict: Overall, the user shows a solid grasp of the task requirements with potential for deeper exploration of Pandas functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient performance by reading the CSV file, dropping unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could explore optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows good performance efficiency with opportunities for optimization in handling larger datasets.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Pandas for data manipulation tasks, demonstrating proficiency in dropping columns, renaming columns, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more complex data transformations and optimizations.
  • Final Verdict: Solid Pandas skills displayed with opportunities for exploring advanced data manipulation techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in reading CSV files, data cleaning, and saving cleaned dataframes.
  • Area of Improvement: To further enhance Python skills, the user could delve into advanced data processing techniques and error handling.
  • Final Verdict: Strong Python skills demonstrated with potential for growth in advanced data processing techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning, transformation, and analysis.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on enhancing data visualization skills and statistical analysis techniques.
  • Final Verdict: Good alignment with the Data Analyst role, with potential for growth in data visualization and statistical analysis.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct syntax and adheres to Python coding standards. Indentation and spacing are consistent.
  • Area of Improvement: Ensuring consistent commenting style and formatting could further enhance the code's readability.
  • Final Verdict: The code syntax is good, with minor improvements possible in commenting consistency.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive, and the steps are well outlined.
  • Area of Improvement: One area of improvement could be to add more comments to explain the reasoning behind certain steps or choices in the code.
  • Final Verdict: Overall, the code clarity is good, but additional comments could enhance understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which aids in understanding the code flow.
  • Area of Improvement: Adding more detailed comments to clarify the purpose of each line of code would enhance the overall readability.
  • Final Verdict: While there are some comments present, more detailed explanations would improve code comprehension.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading, cleaning, and saving the DataFrame.
  • Area of Improvement: Further exploration of advanced Pandas functions and error handling could enhance the user's task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task with room for growth in more complex data manipulation.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, clean, and save the DataFrame. The operations are performed effectively.
  • Area of Improvement: To further improve performance efficiency, optimizing the renaming of columns could be beneficial.
  • Final Verdict: The code shows good performance efficiency with room for optimization in column renaming.
Role And Skill Based Rating
Python
  • Rating: 8
  • Positive Feedback: The user has effectively utilized Python for data manipulation tasks, showcasing proficiency in handling DataFrames.
  • Area of Improvement: Further exploration of advanced Python libraries for data analysis could enhance the user's Python skills.
  • Final Verdict: Solid Python skills demonstrated with potential for growth in utilizing more advanced data analysis techniques.
Pandas
  • Rating: 9
  • Positive Feedback: The user has effectively used Pandas functions to read, clean, and save the DataFrame, showcasing strong Pandas skills.
  • Area of Improvement: Exploring more advanced Pandas functionalities and error handling could further enhance the user's Pandas proficiency.
  • Final Verdict: Excellent Pandas skills demonstrated with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, involving data cleaning and manipulation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could focus on exploring more complex data analysis techniques and tools.
  • Final Verdict: Good alignment with the Data Analyst role, with opportunities for growth in advanced data analysis skills.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct with proper indentation and adherence to Python coding standards. The solution runs without any syntax errors.
  • Area of Improvement: To maintain consistency, the user could ensure uniform spacing and formatting throughout the code.
  • Final Verdict: The code syntax is well-maintained with minor room for improvement in formatting consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and the solution addresses the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code.
  • Final Verdict: Overall, the code clarity is good with room for improvement in adding more comments for better understanding.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the renaming of columns, which aids in understanding the code flow.
  • Area of Improvement: To improve the commenting, the user should consider adding comments for each major step in the code to provide a comprehensive explanation.
  • Final Verdict: While there are some comments present, more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the dataset, removing unwanted columns, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further enhance task understanding, the user could explore handling exceptions and edge cases that may arise during data manipulation.
  • Final Verdict: The user shows a strong grasp of the task requirements with minor areas for improvement in handling potential data issues.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of Pandas functions to read, manipulate, and save the DataFrame. The solution is concise and achieves the task requirements effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the code for larger datasets or handling potential errors.
  • Final Verdict: The code shows good performance efficiency with effective utilization of Pandas functions.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates a strong command of Python programming skills in solving the task requirements.
  • Area of Improvement: To further excel in Python, the user could explore advanced concepts and libraries for more complex data manipulation tasks.
  • Final Verdict: The Python skills displayed by the user are proficient and align well with the task.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to manipulate the DataFrame and achieve the desired data cleaning tasks.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data processing techniques and optimizations.
  • Final Verdict: The Pandas skills demonstrated by the user are strong and well-suited for data manipulation tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's proficiency in Python and Pandas aligns well with the skills required for a Data Analyst role.
  • Area of Improvement: To excel further as a Data Analyst, the user could focus on statistical analysis, data visualization, and machine learning techniques.
  • Final Verdict: The user's skills are well-suited for a Data Analyst role, with potential for growth in advanced analytical methods.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python coding standards. Indentation and spacing are consistent throughout the code.
  • Area of Improvement: To further improve code syntax, the user could consider adding docstrings to functions and following a consistent naming convention.
  • Final Verdict: The code syntax is good, with minor room for improvement in terms of documentation and naming conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. The variable names are descriptive, and the steps are well-defined.
  • Area of Improvement: One area of improvement could be to add comments to explain the purpose of each step in the code for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding comments would enhance it further.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the main steps, which is helpful for understanding the code flow.
  • Area of Improvement: To improve the commenting, the user could add more detailed comments to explain the rationale behind each operation and the expected outcomes.
  • Final Verdict: While there are some comments present, adding more detailed explanations would enhance the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by successfully reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To enhance task understanding, the user could provide more detailed comments to explain the reasoning behind each step and potential challenges faced.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with room for improvement in providing detailed explanations.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently reading the CSV file, dropping the specified column, renaming columns, and saving the cleaned DataFrame.
  • Area of Improvement: To further improve performance efficiency, the user could consider optimizing the code for larger datasets or implementing error handling.
  • Final Verdict: The code shows efficient handling of the data processing tasks.
Role And Skill Based Rating
Pandas
  • Rating: 9
  • Positive Feedback: The user has shown a good understanding of Pandas library functions and methods to read, clean, and save DataFrame.
  • Area of Improvement: To improve Pandas skills, the user could delve into more complex data transformations and optimizations.
  • Final Verdict: The user exhibits proficiency in Pandas for data manipulation tasks with room for advancement in advanced techniques.
Python
  • Rating: 9
  • Positive Feedback: The user has demonstrated proficiency in Python coding by effectively using Pandas functions to manipulate the DataFrame.
  • Area of Improvement: To further enhance Python skills, the user could explore more advanced Pandas functionalities and practice writing efficient code.
  • Final Verdict: The user shows strong Python skills with potential for growth in advanced data manipulation techniques.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's code submission aligns well with the responsibilities of a Data Analyst by focusing on data cleaning and preparation tasks.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance data visualization skills and explore statistical analysis techniques.
  • Final Verdict: The user shows potential for growth in the role of a Data Analyst with a solid foundation in data manipulation tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows Python best practices. The user maintains consistent spacing and indentation, making the code easy to read and understand.
  • Area of Improvement: To maintain code syntax consistency, the user could ensure uniform naming conventions for variables and adhere to PEP 8 guidelines for Python code styling.
  • Final Verdict: The code syntax is well-maintained with minor suggestions for consistency improvements.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and easy to follow. Variable names are descriptive and relevant to their purpose. The user correctly reads the CSV file, removes unwanted columns, renames columns, and saves the cleaned DataFrame. The solution code aligns well with the task requirements.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of each step in the code. Additionally, ensuring consistent formatting and indentation throughout the code would improve readability.
  • Final Verdict: Overall, the code clarity is good, with room for minor improvements in commenting and formatting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of removing unwanted columns and renaming columns. The comments provided are clear and relevant to the code execution.
  • Area of Improvement: To improve the commenting aspect, the user could add more detailed comments to describe each step in the data cleaning process. This would enhance the understanding of the code for other developers.
  • Final Verdict: Overall, the code has adequate comments, but additional detailed comments would further enhance clarity.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user demonstrates a good understanding of the task requirements by successfully importing the CSV file, removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The code execution aligns well with the task instructions.
  • Area of Improvement: To further enhance task understanding, the user could explore different methods for data cleaning and consider handling potential errors during the process.
  • Final Verdict: The user shows a strong understanding of the task with minor areas for improvement in exploring alternative data cleaning approaches.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates good performance efficiency by efficiently removing unwanted columns, renaming columns, and saving the cleaned DataFrame. The user's code executes the task effectively without unnecessary complexities.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the method used for removing unwanted columns and renaming columns to potentially reduce processing time.
  • Final Verdict: The code shows efficient performance with minor opportunities for optimization.
Role And Skill Based Rating
Python
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in Python by effectively using Pandas for data manipulation and cleaning. The code execution showcases a strong command of Python programming.
  • Area of Improvement: To further enhance Python skills, the user could explore advanced Pandas functionalities and data analysis techniques to deepen expertise in data processing.
  • Final Verdict: The user exhibits a high level of Python proficiency with opportunities for skill advancement in advanced data manipulation.
Pandas
  • Rating: 9
  • Positive Feedback: The user effectively utilizes Pandas functions to read, clean, and save the DataFrame. The code implementation reflects a strong understanding of Pandas data manipulation.
  • Area of Improvement: To further enhance Pandas skills, the user could explore more advanced data cleaning techniques and optimization methods within Pandas.
  • Final Verdict: The user demonstrates proficiency in Pandas with potential for skill enhancement in advanced data processing.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by effectively importing and cleaning the dataset. The code execution reflects skills relevant to data analysis tasks.
  • Area of Improvement: To further align with the Data Analyst role, the user could delve into exploratory data analysis techniques and statistical analysis to broaden analytical capabilities.
  • Final Verdict: The user exhibits capabilities relevant to a Data Analyst role with opportunities for skill development in advanced data analysis.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed, and there are no syntax errors present.
  • Area of Improvement: To further improve code syntax, the user could consider consistent formatting practices, such as indentation for better readability.
  • Final Verdict: The code syntax is accurate and aligns with SQL conventions.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively retrieves the required data from the 'users' dataset and sorts it by the 'created_at' column in ascending order. The use of SQL syntax within a Jupyter Notebook cell is appropriate.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the query and the expected output. Additionally, providing a brief explanation of the SQL query logic would improve readability for others.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature. The SQL query itself is clear in its purpose and execution.
  • Area of Improvement: Adding comments to explain the query's objective, the significance of retrieving the 5 oldest users, and any specific considerations in the data would improve the overall documentation of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would enhance its clarity and maintainability.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the 5 oldest users from the 'users' dataset. The SQL query effectively sorts the data by the 'created_at' column and limits the results as specified.
  • Area of Improvement: To enhance task understanding further, the user could explore additional SQL functionalities or data manipulation techniques to expand their skills in querying and analyzing datasets.
  • Final Verdict: The user has shown a strong grasp of the task requirements and executed the query effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the 5 oldest users by sorting the 'created_at' column in ascending order and limiting the results to 5 rows. The query execution is straightforward and achieves the desired outcome effectively.
  • Area of Improvement: To further enhance performance efficiency, the user could explore optimizing the query for larger datasets by considering indexing strategies or alternative sorting techniques.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the 5 oldest users.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to retrieve and sort data from the 'users' dataset based on the 'created_at' column. The SQL query was correctly structured and executed.
  • Area of Improvement: To further enhance MySQL skills, the user could explore more advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user demonstrated proficiency in MySQL for the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user effectively analyzed and retrieved data from the 'users' dataset to identify the 5 oldest users. This aligns with the responsibilities of a Data Analyst in handling and interpreting data.
  • Area of Improvement: To further excel as a Data Analyst, the user could enhance their SQL skills, particularly in data manipulation and querying for more complex analyses.
  • Final Verdict: The user demonstrated proficiency in data analysis tasks relevant to the role of a Data Analyst.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. It is well-structured and easy to read.
  • Area of Improvement: Ensure consistent formatting and indentation throughout the code for better readability.
  • Final Verdict: The code demonstrates strong syntax adherence with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with meaningful variable names and a logical structure. It correctly retrieves the day of the week and the count of registrations from the 'users' table.
  • Area of Improvement: To enhance code clarity further, consider adding comments to explain the purpose of the SQL query and the expected output.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to its simplicity. The query's purpose can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the query's logic and the expected output would enhance the code's readability and maintainability.
  • Final Verdict: While the code is understandable without comments, adding them would improve its overall quality.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct SQL query structure and logic.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functions and optimizations for similar tasks.
  • Final Verdict: The user demonstrates a solid grasp of the task requirements with room for further exploration and improvement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently retrieves the required information by grouping and ordering the data based on the day of the week. It performs the task effectively.
  • Area of Improvement: To improve performance efficiency, optimize the SQL query for better execution speed and resource utilization.
  • Final Verdict: The code is efficient in achieving the task requirements with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for extracting the day of the week with the highest registrations.
  • Area of Improvement: Continue to practice and explore more advanced MySQL functions and optimizations for data analysis tasks.
  • Final Verdict: The user demonstrates proficiency in MySQL for the given task requirements.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract and analyze data effectively.
  • Area of Improvement: To further excel as a Data Analyst, consider exploring more complex data analysis techniques and tools.
  • Final Verdict: The user demonstrates skills relevant to the role of a Data Analyst with potential for growth and development.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: To maintain consistency, the user could ensure proper indentation and spacing throughout the code.
  • Final Verdict: The code syntax is good, with minor suggestions for maintaining consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code provided by the user is clear and concise. It effectively combines the 'users' and 'photos' datasets using a LEFT JOIN and filters for users who have never posted a photo.
  • Area of Improvement: To enhance code clarity further, the user could consider adding comments to explain the purpose of the SQL query and the logic behind the JOIN and WHERE clauses.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments to explain the logic and reasoning behind the SQL query. However, the query itself is relatively straightforward.
  • Area of Improvement: Adding comments to describe the purpose of the query and the significance of the conditions used would enhance the readability and understanding of the code.
  • Final Verdict: While the code is understandable without comments, adding descriptive comments would improve the overall clarity.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a strong understanding of the task requirements by effectively using a LEFT JOIN and WHERE clause to identify inactive users who have never posted a photo.
  • Area of Improvement: To further enhance task understanding, the user could explore more advanced SQL concepts for data manipulation and filtering.
  • Final Verdict: The user has shown a high level of task understanding with potential for growth in exploring advanced SQL functionalities.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries to identify inactive users who have never posted a photo. The LEFT JOIN and WHERE clause are used effectively to filter the desired results.
  • Area of Improvement: To further improve performance efficiency, the user could optimize the query for larger datasets by considering indexing or other optimization techniques.
  • Final Verdict: The code shows high performance efficiency with potential for optimization in handling larger datasets.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively used MySQL queries to combine datasets and filter results, demonstrating proficiency in MySQL for data analysis.
  • Area of Improvement: To enhance MySQL skills, the user could explore more advanced query optimization techniques and database management concepts.
  • Final Verdict: The user's MySQL skills are strong and well-applied in the task solution.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL queries to analyze and filter data, showcasing skills relevant to a Data Analyst role. The understanding of data manipulation and filtering is evident in the code.
  • Area of Improvement: To further excel in the Data Analyst role, the user could explore more complex SQL operations and data visualization techniques.
  • Final Verdict: The user's performance aligns well with the responsibilities of a Data Analyst, with potential for growth in advanced data analysis skills.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query is well-structured and easy to read.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query. Double-check for any potential syntax errors or typos.
  • Final Verdict: The code syntax is good, with minor improvements needed for consistent formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query retrieves the total number of likes for each photo by joining the 'photos' and 'likes' tables and grouping the results by 'photo_id'. The code structure is well organized.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the JOIN and GROUP BY operations. Additionally, provide more descriptive aliases for tables to enhance readability.
  • Final Verdict: Overall, the code clarity is good, but adding more comments and improving table aliases can further enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the query and the tables being used.
  • Area of Improvement: Enhance the comments by providing more detailed explanations of the SQL operations and the expected output. Include comments for each major step in the query.
  • Final Verdict: While there are some comments present, adding more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly retrieving the total number of likes for each photo using a SQL query.
  • Area of Improvement: Further enhance task understanding by exploring different SQL functions or optimizations that could be applied to achieve the same result.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and successfully implemented the solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The SQL query efficiently retrieves the total number of likes for each photo by using a LEFT JOIN and GROUP BY clause. The query is structured to optimize performance and provide accurate results.
  • Area of Improvement: Consider optimizing the query further by checking for any redundant operations or unnecessary columns being selected.
  • Final Verdict: The code demonstrates good performance efficiency in retrieving the total number of likes for each photo.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user has effectively utilized MySQL to retrieve the total number of likes for each photo, showcasing proficiency in SQL queries and database operations.
  • Area of Improvement: Continue to explore advanced MySQL functionalities and optimizations to further enhance query performance.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by efficiently retrieving and analyzing data from the 'photos' and 'likes' tables.
  • Area of Improvement: Further develop skills in data manipulation and analysis to handle more complex queries and datasets.
  • Final Verdict: The user has demonstrated proficiency in tasks related to Data Analysis, with potential for growth in handling more advanced data operations.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices. The query structure is well-formed and easy to read.
  • Area of Improvement: No syntax errors found in the code.
  • Final Verdict: The code syntax is good, maintaining readability and adherence to SQL standards.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to calculate the average user posting frequency. The use of subqueries and the ROUND() function is appropriate for the task.
  • Area of Improvement: Consider adding comments to explain the purpose of each subquery and the rounding operation for better clarity and understanding.
  • Final Verdict: Overall, the code clarity is good, but adding some comments for explanation would enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of each subquery and the rounding operation would improve the overall readability and maintainability of the code.
  • Final Verdict: While the code is clear without comments, adding some comments would enhance the code's documentation and understanding.
Task Understanding
  • Rating: 9
  • Positive Feedback: The user has demonstrated a good understanding of the task by correctly calculating the average user posting frequency using SQL subqueries and the ROUND() function.
  • Area of Improvement: Further improvement could involve adding comments to explain the logic behind each subquery and the rounding operation.
  • Final Verdict: Overall, the user has shown a strong grasp of the task requirements and implemented the solution effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance, using subqueries to directly calculate the ratio of photos to users without unnecessary complexity.
  • Area of Improvement: No major issues found in performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task requirements.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user has effectively utilized MySQL queries to calculate the average user posting frequency, showcasing proficiency in MySQL.
  • Area of Improvement: Consider adding comments for better documentation and explanation of the SQL queries.
  • Final Verdict: Strong MySQL skills demonstrated in solving the task.
Data Analyst
  • Rating: 9
  • Positive Feedback: The task aligns well with the responsibilities of a Data Analyst, involving data analysis and SQL queries to derive insights.
  • Area of Improvement: Adding comments for better documentation and explanation would further enhance the role alignment.
  • Final Verdict: The user has effectively demonstrated skills relevant to the Data Analyst role in this task.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, following SQL standards with proper formatting, indentation, and usage of keywords.
  • Area of Improvement: To maintain consistency, ensure consistent casing for SQL keywords and identifiers throughout the code.
  • Final Verdict: The code demonstrates good syntax adherence and readability.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, following the task requirements effectively. The SQL query selects the username and the count of image URLs from the 'users' and 'photos' tables, respectively. The JOIN operation and GROUP BY clause are used correctly.
  • Area of Improvement: Consider adding comments to explain the purpose of the SQL query and the reasoning behind each step. Additionally, providing more descriptive variable names can enhance code clarity further.
  • Final Verdict: Overall, the code demonstrates good clarity and understanding of the task requirements.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of the SQL query and the usage of certain clauses like JOIN and GROUP BY.
  • Area of Improvement: To enhance code understandability, consider adding more detailed comments to describe the logic behind each step and the expected output of the query.
  • Final Verdict: While there are some comments present, additional detailed comments can improve code comprehensibility.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has a strong understanding of the task requirements, as evidenced by the correct implementation of the SQL query to rank users by the number of postings.
  • Area of Improvement: To further enhance task understanding, consider exploring more advanced SQL functionalities and optimizations for similar tasks.
  • Final Verdict: The user has shown a high level of understanding in fulfilling the task requirements effectively.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently retrieves the desired information by joining the 'users' and 'photos' tables and ordering the results based on the count of photos. The query is structured well for optimal performance.
  • Area of Improvement: To further improve performance efficiency, consider optimizing the query for large datasets by indexing columns used in JOIN and WHERE clauses.
  • Final Verdict: The code shows high performance efficiency in retrieving user rankings based on postings.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for ranking users by postings. The query correctly joins tables, groups data, and orders results as per the task requirements.
  • Area of Improvement: To further excel in MySQL, consider exploring advanced SQL functionalities and optimizations for complex data analysis tasks.
  • Final Verdict: The user has demonstrated proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's understanding of SQL queries and data manipulation aligns well with the responsibilities of a Data Analyst. The ability to extract insights from databases and generate user rankings showcases relevant skills.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring more complex data analysis scenarios and data visualization techniques.
  • Final Verdict: The user has demonstrated proficiency in SQL queries and data analysis, reflecting capabilities required for a Data Analyst role.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 5
  • Positive Feedback: The code structure and syntax follow the correct SQL format.
  • Area of Improvement: While the syntax is correct, the code does not implement the necessary SQL operations to fulfill the task requirements. Enhancements are needed to include the required join, subquery, and grouping operations.
  • Final Verdict: The code syntax is accurate, but it lacks the essential SQL operations to achieve the task objectives.
Code Clarity
  • Rating: 2
  • Positive Feedback: The code attempts to count the total number of posts from the 'photos' table, but it does not consider the relationship between the 'users' and 'photos' tables as required by the task.
  • Area of Improvement: The code needs to include a join operation between the 'users' and 'photos' tables to calculate the total number of posts per user. Additionally, the alias 'total_posts_per_user' is missing, which is essential for grouping and summing the total posts per user.
  • Final Verdict: The code lacks the necessary SQL operations to fulfill the task requirements. It needs to incorporate a join operation and alias for the 'total_posts_per_user' column.
Well Commented
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: There are no comments provided in the code to explain the logic or SQL operations being performed. Adding clear and relevant comments would enhance the readability and understanding of the code.
  • Final Verdict: The code lacks any comments to explain the SQL operations or logic, making it difficult to understand the purpose of each statement.
Task Understanding
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not demonstrate an understanding of the task requirements. It fails to incorporate the necessary SQL operations such as join, subquery, and grouping to calculate the total posts per user.
  • Final Verdict: The code does not fulfill the task requirements and lacks an understanding of the SQL operations needed to determine the total posts per user.
Performance Efficiency
  • Rating: 0
  • Positive Feedback: Everything seems to be good
  • Area of Improvement: The code does not meet the performance efficiency criteria as it does not correctly calculate the total number of posts per user. It lacks the necessary subquery and grouping operations to achieve the desired outcome.
  • Final Verdict: The code does not demonstrate performance efficiency as it fails to implement the required SQL operations for calculating the total posts per user.
Role And Skill Based Rating
MySql
  • Rating: 2
  • Positive Feedback: The code uses MySQL syntax for querying the database tables.
  • Area of Improvement: The MySQL operations in the code need to be expanded to include a join operation, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: While MySQL is used, the code lacks the necessary SQL operations to fulfill the task requirements.
Data Analyst
  • Rating: 1
  • Positive Feedback: The task involves analyzing data from the database tables to determine the total posts per user, which aligns with the responsibilities of a Data Analyst.
  • Area of Improvement: The code needs to incorporate the necessary SQL operations such as join, subquery, and grouping to accurately calculate the total posts per user.
  • Final Verdict: The code partially aligns with the role of a Data Analyst but requires enhancements to fulfill the task requirements.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct, follows SQL standards, and is well-structured.
  • Area of Improvement: No syntax errors or issues found in the code.
  • Final Verdict: The code syntax is well-maintained and adheres to best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, with a straightforward SQL query to count the number of distinct users who have posted at least once. The aliasing of the result adds clarity to the output.
  • Area of Improvement: Consider adding comments to explain the purpose of the query or any specific details that might help others understand the code better.
  • Final Verdict: Overall, the code clarity is good, but adding comments for better understanding would be beneficial.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively simple and self-explanatory due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query or any specific details would enhance the readability and maintainability of the code.
  • Final Verdict: While the code is understandable without comments, adding them would improve the overall quality of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly understood the task requirements and implemented a suitable SQL query to count the users with at least one post.
  • Area of Improvement: Considering the simplicity of the task, the code could have been further enhanced with additional comments for better clarity.
  • Final Verdict: The user has demonstrated a strong understanding of the task requirements with an effective solution.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code is efficient in terms of performance as it directly counts the distinct user IDs from the 'photos' table, which is a direct and optimal approach.
  • Area of Improvement: No specific areas of improvement identified in terms of performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency in achieving the task.
Role And Skill Based Rating
SQL
  • Rating: 9
  • Positive Feedback: The user has effectively utilized SQL to solve the task of counting users with at least one post. The query is well-structured and achieves the desired outcome.
  • Area of Improvement: Enhancing SQL skills further by incorporating more complex queries and optimizing performance could be beneficial.
  • Final Verdict: The user demonstrates a strong proficiency in SQL with the ability to handle basic tasks effectively.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's solution aligns well with the responsibilities of a Data Analyst by analyzing user engagement metrics through SQL queries.
  • Area of Improvement: Further exploration of data analysis techniques and tools could enhance the user's skills in this role.
  • Final Verdict: The user shows competence in applying data analysis techniques to extract meaningful insights from the data.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. The query structure is well-formed and adheres to best practices.
  • Area of Improvement: Ensure consistent indentation and spacing throughout the query for better readability and maintainability.
  • Final Verdict: The code syntax is excellent with minor suggestions for consistency in formatting.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and easy to understand. The SQL query effectively joins the 'tags' and 'photo_tags' datasets, groups the results by 'tags.id', and orders the hashtags by the total number of occurrences.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the joins and grouping. Additionally, aliasing the count as 'total' as mentioned in the task description would enhance clarity.
  • Final Verdict: Overall, the code clarity is good with room for minor improvements in commenting and aliasing.
Well Commented
  • Rating: 6
  • Positive Feedback: The code lacks comments but is relatively straightforward due to the simplicity of the SQL query. The logic can be understood without extensive comments.
  • Area of Improvement: Adding comments to explain the purpose of each part of the query and the reasoning behind the joins and grouping would enhance the overall readability and understanding.
  • Final Verdict: While the code is clear without comments, adding explanatory comments would improve the overall documentation.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements, as evidenced by the correct use of joins, grouping, and ordering to identify the top 5 hashtags.
  • Area of Improvement: To enhance task understanding further, consider aliasing the count as 'total' as specified in the task description and ensure the query precisely limits the results to the top 5 rows.
  • Final Verdict: Overall, the user demonstrates a solid understanding of the task with minor areas for improvement in query refinement.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently joins the datasets, groups the results, and orders the hashtags by total occurrences. The query structure is effective in identifying the top 5 most commonly used hashtags.
  • Area of Improvement: To improve performance efficiency further, consider optimizing the query for better resource utilization and potentially explore ways to enhance the speed of execution.
  • Final Verdict: The code demonstrates good performance efficiency with potential for optimization.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user demonstrates proficiency in MySQL by effectively using SQL queries to join datasets, group results, and order data based on task requirements.
  • Area of Improvement: To further enhance the MySQL skill, the user can explore advanced SQL functionalities and optimization techniques.
  • Final Verdict: The user shows strong MySQL skills with potential for further development in advanced SQL concepts.
Data Analyst
  • Rating: 8
  • Positive Feedback: Completing this task demonstrates skills relevant to a Data Analyst role, such as data querying, analysis, and interpretation.
  • Area of Improvement: To further align with the Data Analyst role, the user can focus on data visualization techniques and statistical analysis.
  • Final Verdict: The user exhibits capabilities suitable for a Data Analyst role with opportunities for growth in data visualization and statistical analysis.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Indentation and spacing are consistent, making the code easy to read.
  • Area of Improvement: Ensure consistent formatting throughout the code and consider adding comments to explain complex logic.
  • Final Verdict: The code syntax is well-maintained with minor areas for improvement in maintaining consistent formatting and adding more comments.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. It effectively joins the 'users' and 'likes' datasets and groups the results by user ID to count the number of likes. The aliasing of the count for clarity is a good practice.
  • Area of Improvement: Consider adding more descriptive variable names for better readability. Additionally, commenting on the purpose of the subqueries and the logic behind them would enhance code clarity.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in variable naming and commenting.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the logic of the subqueries and the overall purpose of the query.
  • Area of Improvement: Enhance the commenting by providing more detailed explanations of the subqueries and the reasoning behind the filtering conditions.
  • Final Verdict: While there are some comments present, more detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The code effectively identifies users who have liked every single photo on the site, fulfilling the task requirements.
  • Area of Improvement: Further understanding of SQL optimization techniques could enhance the performance efficiency of the code.
  • Final Verdict: The user demonstrates a good understanding of the task requirements with potential for improvement in optimizing SQL queries for better performance.
Performance Efficiency
  • Rating: 7
  • Positive Feedback: The code efficiently uses SQL queries to achieve the task requirements. It effectively filters users who have liked every single photo on the site.
  • Area of Improvement: Optimize the query for better performance by minimizing the number of subqueries and ensuring efficient indexing for faster execution.
  • Final Verdict: The code is efficient in achieving the task, but there is room for improvement in optimizing the query for better performance.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySql skills to join datasets, filter data, and group results in the SQL solution code.
  • Area of Improvement: Further optimization of MySql queries for performance efficiency could enhance the code.
  • Final Verdict: The user demonstrates proficiency in MySql skills with room for improvement in query optimization.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's solution code aligns well with the responsibilities of a Data Analyst by analyzing and filtering data to identify potential bots.
  • Area of Improvement: Further optimization of SQL queries and enhancing code clarity could improve the alignment with Data Analyst roles.
  • Final Verdict: The user demonstrates proficiency in tasks relevant to a Data Analyst role with opportunities for improvement in SQL query optimization.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code is free from syntax errors and follows proper SQL syntax conventions. It adheres to best practices in terms of spacing and indentation.
  • Area of Improvement: Consider consistent formatting for SQL keywords and identifiers to enhance code readability.
  • Final Verdict: The code demonstrates good syntax with minor formatting improvements possible.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is clear and concise, using appropriate variable names and SQL syntax. It effectively selects the 'username' and 'comment_text' from the 'users' and 'comments' tables respectively.
  • Area of Improvement: Consider adding comments to explain the purpose of the query and the logic behind the LEFT JOIN operation for better clarity and understanding.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor improvements in commenting.
Well Commented
  • Rating: 7
  • Positive Feedback: The code lacks comments but is relatively simple and easy to understand due to its concise nature.
  • Area of Improvement: Adding comments to explain the purpose of the query and the significance of the LEFT JOIN operation would enhance the code's readability and maintainability.
  • Final Verdict: While the code is clear, adding comments would improve its overall quality and understanding.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has correctly identified the users who have never commented by utilizing a LEFT JOIN and filtering for NULL values in 'comment_text'. The query fulfills the task requirements effectively.
  • Area of Improvement: Further enhancing the understanding of SQL JOIN operations and query optimization could lead to even more efficient solutions in the future.
  • Final Verdict: The user has a strong grasp of the task requirements with minor room for improvement in SQL query optimization.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently uses a LEFT JOIN operation to identify users who have never commented on a photo. It correctly filters records where 'comment_text' is NULL, achieving the desired outcome.
  • Area of Improvement: Optimization could be done by explicitly selecting only the necessary columns instead of using '*' in the SELECT statement.
  • Final Verdict: The code shows high efficiency in achieving the task requirements with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data retrieval task. The SQL query correctly identifies users who have never commented on a photo.
  • Area of Improvement: Continuing to practice and enhance SQL skills will further strengthen the user's proficiency in database querying.
  • Final Verdict: The user has demonstrated excellent proficiency in MySQL for the given task.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's understanding and implementation of SQL for data retrieval align well with the responsibilities of a Data Analyst, showcasing the ability to extract meaningful insights from databases.
  • Area of Improvement: Further practice in optimizing SQL queries and understanding complex JOIN operations can enhance the user's skills as a Data Analyst.
  • Final Verdict: The user's performance indicates a good foundation in SQL for data analysis tasks.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and understand.
  • Area of Improvement: Ensure consistency in naming conventions and formatting throughout the codebase for better maintainability.
  • Final Verdict: The code demonstrates good syntax practices with minor suggestions for consistency.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements. The use of CTEs and subqueries is appropriate for the analysis of user behavior metrics.
  • Area of Improvement: Consider providing more descriptive names for the CTEs and subqueries to enhance code clarity. Additionally, adding comments to explain the logic behind the queries would further improve readability.
  • Final Verdict: Overall, the code demonstrates good clarity with room for minor enhancements in naming conventions and comments.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the queries and calculations. The comments help in understanding the logic behind the code.
  • Area of Improvement: Adding more detailed comments to clarify complex logic and provide insights into the reasoning behind each step would improve the overall comment quality.
  • Final Verdict: While the code has comments for basic understanding, enhancing the comments with more detailed explanations would be beneficial.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has a good understanding of the task requirements and has implemented the necessary SQL queries to analyze user behavior metrics effectively.
  • Area of Improvement: Further refinement in handling edge cases and error scenarios could enhance the overall task understanding.
  • Final Verdict: The user shows a solid grasp of the task with opportunities for improvement in handling exceptional cases.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code efficiently calculates the percentage of users who have never commented on a photo or have liked every photo. The use of subqueries and appropriate joins contributes to the performance efficiency.
  • Area of Improvement: Optimizing the queries further could potentially enhance performance, especially when dealing with large datasets.
  • Final Verdict: The code shows high performance efficiency with minor optimization opportunities.
Role And Skill Based Rating
MySql
  • Rating: 10
  • Positive Feedback: The user effectively utilized MySQL to perform the required data analysis tasks.
  • Area of Improvement: Continue to refine MySQL skills for more complex queries and optimizations.
  • Final Verdict: The user demonstrates strong proficiency in MySQL for data analysis tasks.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's SQL skills align well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data efficiently.
  • Area of Improvement: Further development in Python and Pandas could enhance the user's toolkit for data analysis and manipulation.
  • Final Verdict: The user exhibits strong potential in the role of a Data Analyst with opportunities for skill enhancement in Python and Pandas.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 9
  • Positive Feedback: The code syntax is correct and follows SQL standards. Proper indentation and formatting make the code easy to read and maintain.
  • Area of Improvement: To further improve code syntax, ensure consistent naming conventions for aliases and columns throughout the query.
  • Final Verdict: The code syntax is well-maintained and adheres to SQL best practices.
Code Clarity
  • Rating: 8
  • Positive Feedback: The code is well-structured and follows the task requirements accurately. The use of CTEs (Common Table Expressions) makes the SQL query easy to read and understand.
  • Area of Improvement: Consider adding more descriptive comments to explain the purpose of each CTE and the overall logic of the query. Additionally, providing a brief explanation of the output format could enhance code clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments could further improve readability.
Well Commented
  • Rating: 7
  • Positive Feedback: The code includes some comments to explain the purpose of the CTEs and filtering conditions. The comments provide a basic understanding of the logic.
  • Area of Improvement: To improve the commenting, consider adding comments to explain the output format, the grouping logic, and any specific considerations in the query.
  • Final Verdict: While there are some comments present, additional explanations could enhance the overall understanding of the code.
Task Understanding
  • Rating: 9.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly implementing the SQL query to retrieve the count of comments made by users with non-null comment_text.
  • Area of Improvement: To enhance task understanding, consider exploring more complex SQL queries and scenarios to deepen knowledge and skills in database querying.
  • Final Verdict: The user has shown a strong grasp of the task requirements with minor areas for improvement.
Performance Efficiency
  • Rating: 9
  • Positive Feedback: The code demonstrates efficient use of SQL queries with appropriate joins and filtering conditions. The use of CTEs helps optimize the query execution.
  • Area of Improvement: To further enhance performance efficiency, consider optimizing the query by analyzing the execution plan and identifying potential areas for optimization.
  • Final Verdict: The code shows good performance efficiency with well-structured SQL queries.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL to write the SQL query for the task, demonstrating proficiency in database querying.
  • Area of Improvement: To further enhance MySQL skills, consider exploring more advanced SQL concepts and optimization techniques.
  • Final Verdict: The user has shown strong MySQL skills in implementing the required SQL query.
Data Analyst
  • Rating: 9
  • Positive Feedback: The user's implementation of the SQL query aligns well with the responsibilities of a Data Analyst, showcasing the ability to extract insights from data.
  • Area of Improvement: To further excel in the role of a Data Analyst, consider exploring data visualization techniques and statistical analysis.
  • Final Verdict: The user has demonstrated skills relevant to the role of a Data Analyst through effective database querying.

Task Description

Identifying Inactive Users

Find the users who have never posted a photo, as we want to target our inactive users with an email campaign.

  • Combine the 'users' and 'photos' datasets using a LEFT JOIN, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the WHERE clause, filter for rows where the 'id' from 'photos' is NULL, indicating that the user has never posted a photo.
  • Select the 'username' of users who meet this criteria to identify the users who have never posted a photo.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining Comments Data

  • Import Pandas as 'pd'.
  • Read the CSV file comments.csv into a Pandas DataFrame named 'comments'.
  • To import the 'comments.csv' file, which is located in the root path of your project, you should use the following path: './comments.csv'.
  • Define a list of unwanted columns to remove (e.g., 'posted date', 'emoji used', 'Hashtags used count').
  • Use the .drop() method to remove unwanted columns from the 'comments' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'comment' to 'comment_text', 'User id' to 'user_id', 'Photo id' to 'photo_id', 'created Timestamp' to 'created_at').
  • Use the .rename() method to rename the columns in the 'comments' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter as index=False to save the cleaned DataFrame as 'comments_cleaned.csv'.
  • Inspect the data by calling the variable 'comments'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Peak Registration Day

What day of the week do most users register on? We need to figure out when to schedule an ad campaign.

  • Use the SQL SELECT statement.
  • Use the date_format function to extract the day of the week ('%W') from the 'created_at' column and rename the result as 'day of the week'.
  • COUNT the occurrences for each day and rename the count as 'total registration'.
  • FROM the 'users' table.
  • GROUP BY the first column (represented as '1' in the query, which is the formatted day of the week).
  • ORDER BY the count of registrations in descending order ('total registration').
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Analyzing User Base

Find the 5 oldest users.

  • Retrieve user data from the 'users' dataset.
  • Sort the results by the 'created_at' column to order users from oldest to newest.
  • Limit the results to 5 rows to obtain the 5 oldest users.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Data Download, Import, and Database Connection.

  • Download the dataset comments_cleaned.csv which is exported in Module 1 - Task 1.
  • Download the dataset follows_cleaned.csv which is exported in Module 1 - Task 2.
  • Download the dataset likes_cleaned.csv which is exported in Module 1 - Task 3.
  • Download the dataset photo_tags_cleaned.csv which is exported in Module 1 - Task 4.
  • Download the dataset photos_cleaned.csv which is exported in Module 1 - Task 5.
  • Download the dataset tags_cleaned.csv which is exported in Module 1 - Task 6.
  • Download the dataset users_cleaned.csv which is exported in Module 1 - Task 7.
  • If you receive an error message stating that the file is not available while downloading, please rerun the cell responsible for exporting that particular CSV file.
  • Create the table on MYSQL using your credentials provided here
  • Use the provided login information to access the database by clicking the link located on the Databases tab. Once there, you need to upload the required datasets in the specific database mentioned in the database info tab. Rename the tables to comments, follows ,likes, photos, photos_tags,tags and users using the Operations tab within the database interface and then click on ""Run test"" to complete the task.
  • Alternatively, click on the 'Import' button for the ''comments_cleaned.csv'' , 'follows_cleaned.csv', 'likes_cleaned.csv','photo_tags_cleaned.csv','photos_cleaned.csv' ,'tags_cleaned.csv' and 'users_cleaned.csv' files directly to import the file into your database. Table names should be comments, follows ,likes, photos, photos_tags,tags and users
  • Use the %load_ext sql command to load the SQL extension in your Jupyter Notebook environment. This extension allows you to run SQL commands directly within your notebook.
  • Use the %sql magic command to specify the connection string for your MySQL database. Replace <user>, <password>, and <db_name> with your actual database credentials and details.
  • Otherwise, simply replace the provided database connection string in the Databases tab.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining users Data

  • Import Pandas as 'pd'.
  • Read the CSV file users.csv into a Pandas DataFrame named 'users'.
  • To import the 'users.csv' file, which is located in the root path of your project, you should use the following path: './users.csv'.
  • Define a list of unwanted columns to remove (e.g., 'private/public', 'post count', 'Verified status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the unwanted columns from the 'users' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'name' to 'username', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'users' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'users_cleaned.csv'.
  • Inspect the data by calling the variable 'users'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file tags.csv into a Pandas DataFrame named 'tags'.
  • To import the 'tags.csv' file, which is located in the root path of your project, you should use the following path: './tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'location').
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove the 'location' column from the 'tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'tag text' to 'tag_name', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'tags_cleaned.csv'.
  • Inspect the data by calling the variable 'tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining photos Data

  • Import Pandas as 'pd'.
  • Read the CSV file photos.csv into a Pandas DataFrame named 'photos'.
  • To import the 'photos.csv' file, which is located in the root path of your project, you should use the following path: './photos.csv'.
  • Define a list of unwanted columns to remove (e.g., 'Insta filter used', 'photo type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photos' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'id' to 'id', 'image link' to 'image_url', 'user ID' to 'user_id', 'created dat' to 'created_date').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photos' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photos_cleaned.csv'.
  • Inspect the data by calling the variable 'photos'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining photo_tags Data

  • Import Pandas as 'pd'.
  • Read the CSV file photo_tags.csv into a Pandas DataFrame named 'photo_tags'.
  • To import the 'photo_tags.csv' file, which is located in the root path of your project, you should use the following path: './photo_tags.csv'.
  • Define a list of unwanted columns to remove (e.g., 'user id'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'photo_tags' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'photo' to 'photo_id', 'tag ID' to 'tag_id').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'photo_tags' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'photo_tags_cleaned.csv'.
  • Inspect the data by calling the variable 'photo_tags'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining likes Data

  • Import Pandas as 'pd'.
  • Read the CSV file likes.csv into a Pandas DataFrame named 'likes'.
  • To import the 'likes.csv' file, which is located in the root path of your project, you should use the following path: './likes.csv'.
  • Define a list of unwanted columns to remove (e.g., 'following or not', 'like type'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'likes' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'user ' to 'user_id', 'photo' to 'photo_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'likes' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'likes_cleaned.csv'.
  • Inspect the data by calling the variable 'likes'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Importing and Refining follows Data

  • Import Pandas as 'pd'.
  • Read the CSV file follows.csv into a Pandas DataFrame named 'follows'.
  • To import the 'follows.csv' file, which is located in the root path of your project, you should use the following path: './follows.csv'.
  • Define a list of unwanted columns to remove (e.g., 'is follower active', 'followee Acc status'). Replace with the actual column names to remove.
  • Use the .drop() method with parameters labels=unwanted_columns and axis=1 to remove unwanted columns from the 'follows' DataFrame.
  • Define a dictionary to rename the columns (e.g., 'follower' to 'follower_id', 'followee ' to 'followee_id', 'created time' to 'created_at').
  • Use the .rename() method with parameter columns=new_column_names to rename the columns in the 'follows' DataFrame using the dictionary.
  • Use the .to_csv() method with parameter index=False to save the cleaned DataFrame as 'follows_cleaned.csv'.
  • Inspect the data by calling the variable 'follows'.

Note: Make sure to comment out the to_csv() function line of code that is responsible for exporting the csv file before running the test case ("Run Test").

Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Retrieve the Total Number of Likes for Each Photo

Retrieve the total number of likes for each photo.

  • Select "photo_id" (p.id) and count the number of likes by using a LEFT JOIN operation. Join the "photos" table (aliased as 'p') with the "likes" table (aliased as 'l') on the photo's "id."
  • Use the GROUP BY clause to group the results by "photo_id."
  • This will provide the total number of likes for each photo in your Instagram Insights project.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Average User Posting Frequency

Our investors want to know how many times the average user posts, which is calculated as the total number of photos divided by the total number of users.

  • Use the SQL SELECT statement.
  • Calculate the ratio of the total count of photos to the total count of users.
  • Use two subqueries: one to count the total number of photos and another to count the total number of users.
  • Divide the count of photos by the count of users.
  • Round the result to two decimal places using the ROUND() function.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

User Ranking by Postings

Rank users by the number of postings, from highest to lowest.

  • Use SELECT statement to select the username from the users table and the count of image_url from the photos table.
  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • Apply the GROUP BY clause to group the results by the user's ID (users.id)
  • Order the results by the count of photos (the second column) in descending order to rank users from highest to lowest based on the number of postings.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Total Posts by Users

Determine the total number of posts by users.

  • Use the SQL SELECT statement.
  • Calculate the total sum of the 'total_posts_per_user' column.
  • Within a subquery, join the 'users' table with the 'photos' table based on the 'id' and 'user_id' columns, respectively.
  • Count the number of posts per user (number of occurrences of 'image_url') and alias it as 'total_posts_per_user.'
  • Group the data by 'users.id'.
  • In the main query, sum the 'total_posts_per_user' calculated from the subquery.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Count of Users with At Least One Post

Determine the total number of users who have posted at least one time.

  • Join the 'users' and 'photos' datasets to associate users with their photos, linking them on the 'id' of 'users' and the 'user_id' of 'photos.'
  • In the query, count the number of distinct 'users.id' to find the total number of users who have posted at least one time.
  • Alias the result as 'total_number_of_users_with_posts' for clarity.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Identifying Top 5 Hashtags

A brand wants to know which hashtags to use in a post. What are the top 5 most commonly used hashtags?

  • Join the 'tags' and 'photo_tags' datasets to associate tags with photos, linking them on the 'id' of 'tags' and the 'tag_id' of 'photo_tags.'
  • Group the results by 'tags.id' to count the number of times each hashtag is used.
  • In the query, select 'tag_name' and count the occurrences of each hashtag. Alias the count as 'total' for clarity.
  • Order the results by 'total' in descending order to identify the most commonly used hashtags.
  • Limit the results to the top 5 rows to provide the brand with the top 5 most commonly used hashtags they can consider for their posts.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Identifying Potential Bots

Identify users who have liked every single photo on the site, as there is a problem with bots.

  • Join the 'users' and 'likes' datasets to associate users with their likes, linking them on the 'id' of 'users' and the 'user_id' of 'likes.'
  • Group the results by 'users.id' to count the number of likes made by each user.
  • In the query, select 'users.id,' 'username,' and count the number of likes by each user. Alias the count as 'total_likes_by_user' for clarity.
  • Use the HAVING clause to filter for rows where 'total_likes_by_user' is equal to the total number of photos on the site, which can be obtained by using a subquery: (SELECT COUNT(*) FROM photos).
  • This query will identify users who have liked every single photo on the site and are potentially bots, as they have an unusually high number of likes.
In SQL projects, We are using Jupyter Notebooks for execution. To run SQL commands, simply include %%sql at the beginning of code cells.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Identifying Users Who Have Never Commented

Identify users who have never commented on a photo, as there is a problem with celebrities.

  • Use the SQL SELECT statement.
  • Select 'users.username' and 'comments.comment_text'.
  • Perform a LEFT JOIN between the 'users' table and the 'comments' table using 'users.id' and 'comments.user_id' as the joining condition.
  • Filter the results to retrieve records where 'comment_text' in the 'comments' table is NULL.
  • This query fetches 'username' from the 'users' table and corresponding 'comment_text' from the 'comments' table where the comment is missing (i.e., NULL) for users.
 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on every photo.

  • Use the SQL SELECT statement.
  • Define two subqueries, tableA and tableB, each containing a separate calculation related to user behavior metrics.
  • For tableA, count the total number of users who have never commented by using a LEFT JOIN between the 'users' table and the 'comments' table where 'comment_text' is NULL. Perform a GROUP BY on 'users.id' and count the distinct users.
  • For tableB, count the total number of users who have liked every photo. This involves joining the 'users' table with the 'likes' table based on 'user_id.' Group the data by 'users.id' and filter it using HAVING to include only those users whose count of distinct liked photos matches the total count of photos in the 'photos' table.
  • Calculate the percentage of users who like every photo by multiplying the count obtained in tableB by 100 and dividing it by the total count of users obtained from the subquery.
Hint:For tableA, use a LEFT JOIN between 'users' and 'comments,' count the users with NULL comments, and alias it as 'total_A.' For tableB, join 'users' with 'likes,' group by 'users.id,' filter using HAVING to include users who liked every photo, and calculate the percentage directly within the subquery as 'total_B.' Join the results of tableA and tableB using a CROSS JOIN to present the final output.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Identifying Users Who Have Commented

Retrieve the count of comments made by users who have posted comments (non-null comment_text), grouping the count by each user's username

  • Use the SQL SELECT statement.
  • Select 'TEMP2.username' and count occurrences using COUNT(*).
  • Start by creating a subquery named TEMP to join the 'users' table with the 'comments' table using a LEFT JOIN based on 'users.id' and 'comments.user_id'.
  • Within TEMP, filter for rows where 'comment_text' is not NULL using the HAVING clause.
  • From the derived table TEMP, another subquery TEMP2 is formed to select 'TEMP.username' and 'TEMP.comment_text'.
  • Finally, in the main query, group the results by 'TEMP2.username' and count the occurrences for each user.
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.

Task Description

Analyzing Bot and Celebrity Accounts

Are we overrun with bots and celebrity accounts? Find the percentage of our users who have either never commented on a photo or have commented on photos before.

  • Use the SQL SELECT statement.
  • Calculate various metrics related to commenting behavior percentages.
  • In the subquery TEMP, perform a LEFT JOIN between the 'users' table and the 'comments' table based on 'users.id' and 'comments.user_id'.
  • Within TEMP, count the distinct users and evaluate the presence of NULL values in 'comment_text' using a CASE statement.
  • Calculate %Celebrity_count by finding the percentage of users who have never commented (based on the presence of NULL 'comment_text') using the SUM() and COUNT() functions.
  • Calculate the Number Of Users Who Never Commented by rounding the %Celebrity_count value.
  • Calculate %Bot_count by subtracting %Celebrity_count from 100.
  • Calculate the Number Of Users Who Always Commented by subtracting the rounded Number Of Users Who Never Commented from 100.

Hint:In the subquery TEMP, perform a LEFT JOIN between 'users' and 'comments'. In the main query, use 100 * SUM(CASE WHEN TEMP.comment_text IS NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT TEMP.id) for %Celebrity_count. Round the result for 'Number Of Users Who Never Commented'. Calculate %Bot_count by subtracting %Celebrity_count from 100. Derive 'Number Of Users Who Always Commented' by subtracting the rounded 'Number Of Users Who Never Commented' from 100

 
Performance Based Rating
Code Syntax
  • Rating: 8
  • Positive Feedback: The code follows correct SQL syntax and adheres to best practices in formatting and indentation.
  • Area of Improvement: Ensuring consistent formatting and indentation throughout the code could further enhance code syntax.
  • Final Verdict: The code maintains good SQL syntax with proper formatting and adherence to best practices.
Code Clarity
  • Rating: 7
  • Positive Feedback: The code has clear variable names and follows the task requirements. The SQL query structure is well-defined and understandable.
  • Area of Improvement: Consider adding more comments to explain the logic behind the calculations. Enhancing the readability of the code by breaking down complex calculations into smaller steps could improve clarity.
  • Final Verdict: Overall, the code clarity is good, but additional comments and simplification of complex calculations could enhance readability.
Well Commented
  • Rating: 6
  • Positive Feedback: The code includes some comments to explain the purpose of calculations and SQL operations.
  • Area of Improvement: Adding more detailed comments to clarify the logic behind each calculation step would enhance the understandability of the code.
  • Final Verdict: While there are some comments present, additional detailed explanations would improve the overall clarity of the code.
Task Understanding
  • Rating: 8.5
  • Positive Feedback: The user has demonstrated a good understanding of the task requirements by correctly calculating the metrics related to commenting behavior percentages.
  • Area of Improvement: Further refinement in the SQL query to optimize the calculations and improve readability could enhance the task understanding.
  • Final Verdict: Overall, the user has shown a solid grasp of the task requirements with accurate metric calculations.
Performance Efficiency
  • Rating: 8
  • Positive Feedback: The code efficiently calculates the required metrics using SQL functions. The use of aggregate functions and grouping is appropriate for the task.
  • Area of Improvement: Optimizing the SQL query further to reduce redundancy in calculations could improve performance efficiency.
  • Final Verdict: The code demonstrates good performance efficiency with effective utilization of SQL functions for metric calculations.
Role And Skill Based Rating
MySql
  • Rating: 9
  • Positive Feedback: The user effectively utilized MySQL queries to calculate the required metrics as per the task instructions.
  • Area of Improvement: Further optimization of the SQL query for performance efficiency could enhance the MySQL skill rating.
  • Final Verdict: The user has demonstrated proficiency in MySQL by accurately implementing the required calculations.
Data Analyst
  • Rating: 8
  • Positive Feedback: The user's approach to analyzing user commenting behavior aligns well with the responsibilities of a Data Analyst.
  • Area of Improvement: Enhancing the clarity and efficiency of the SQL query could further strengthen the Data Analyst skill rating.
  • Final Verdict: The user has showcased skills relevant to a Data Analyst role through the analysis of user commenting behavior.