The time is ripe to become a data scientist because businesses across a variety of industries are getting into analytics as a way to gather and leverage smarter business intelligence. Aspiring to be a data scientist is a path to a career that's in sync with technological disruptions in the near future. It's clear that the field is rapidly evolving and can grow along with the evolving technology landscape.
In fact, the US Bureau of Labor Statistics estimates that by 2026, there are going to be 11.5 million positions in data science and analysis. This is only the tip of the Iceberg. In the coming years, the position of a full-stack Data Scientist will transform the entire world in a million ways.
So, if you are a data fan, this read is for you. We'll be discussing the steps required to be a Data Scientist along with qualifications such as job description, qualifications, abilities, roles and responsibilities required for success in this field.
The Data Science Domain
Data Science encompasses the extraction of data that is clean from raw data. It then is the analysis of these data to understand it, or more precisely, create useful and actionable insights via visualization. The process of sorting through data is what Data Scientists are trained specifically to perform and they are adept at delivering important insights that can help improve decision-making.
Digital data is thought of as the fuel in the twenty-first century due to its numerous research, business and personal benefits. Everything from your Facebook posts to the latest Google search is crucial for Data Scientists in one way or another at the end of the day. That’s why they say that the Data Science domain deals with all of the massive datasets that make data useful and ready for integration into real-world applications.
Responsibilities of a Data Scientist
The role of a Data Scientist is usually one of the many typical job descriptions, including computer professional, statistician scientist and mathematician.
His/her responsibilities involve the extraction and analysis of vast amounts of data to find patterns and trends which can be beneficial to businesses, individuals as well as organizations. They employ various tools for analytics to perform advanced analytics and technology that incorporate predictive modeling as well as Machine Learning. Data Scientists look at issues and create data-driven solutions. Incredibly, even if a machine does not comprehend the information, Data Scientists can find the solution because they use their own judgment to identify patterns.
Qualifications of a Data Scientist
Data Scientists must possess a solid understanding of programming languages such as Python, R, SQL and Machine Learning models as well as sufficient expertise in workflow in Git along with the command line. Beyond that, professionals need reporting capabilities and the ability to communicate effectively to solve problems.
It isn't difficult to step into the job of a Data Scientist with no prior knowledge of the field. It is normal for people to be aspiring to change to Data Analyst positions even if they do not have any prior experience in the field.
Education Requirements of a Data Scientist
A Data Scientist is required to hold at least a bachelor's degree. Higher-level or advanced degrees might not be required for employment (even when job descriptions call for these requirements). A majority of employers are looking for skills that are relevant to the industry. Candidates with less relevant qualifications can enhance their resumes by acquiring the most advanced skills and gaining experience in Data Science projects.
However, the requirements for education do include the completion of an advanced degree in mathematics, computer science, statistics and/or Data Science. There are a variety of certification opportunities available to Data Science aspirants, such as Certified Analytics Professional, Microsoft MCSE Data Management and Analytics, MCSA: Various SQL/Data Engineering options as well as Dell EMC DECA-DS.
Soft Skills Required for a Data Scientist
A skill that is crucial - and which a majority of data scientists do not have - is communication, both in writing and verbal. It's not possible to be a valuable business leader if you lack the ability to effectively communicate with a team to connect the business aspects of data with the scientific and technical aspects. A data scientist must be able to communicate his/her thoughts and objectives with business partners and their leaders in a manner that they can comprehend. It is also essential to be able to function as a member of a team and the communication aspect aids data scientists here. The ability to adapt is another crucial skill. Data Scientists must also be in love with the area they work in.
Certifications Required for a Data Scientist
There are certain areas of study that you should focus your attention on when you think about your career as a data scientist. A background in data science, BI, or even data mining will provide you with a strong foundation for applying analytics or modeling of data. Businesses are doing many activities with their databases at the level of the enterprise. Therefore, you should take a look at educational courses on massive data or data mining or building predictive models as well as Hadoop as well as Spark.
Competencies Needed to be a Data Scientist
In order to be a Data Scientist, you'll have to acquire competencies that include the following:
Develop the knowledge of databases needed to analyze and store data with tools like Oracle(r) Database, MySQL(r), Microsoft(r) SQL Server, and Teradata(r).
Master the concepts of probability, statistics and mathematical analysis.
Be able to master at minimum at least one of the programming languages. Tools for programming like R, Python, and SAS are crucial when it comes to performing analysis on data.
Learn Data Wrangling that requires cleaning and manipulating as well as organizing the data. Popular tools for data manipulation are R, Python, Flume and Scoop.
Learn the fundamentals that comprise Machine Learning, which includes how to use various algorithms like Regressions Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms to name some.
Possessing an understanding of Big Data tools such as Apache Spark, Hadoop, Talend and Tableau that are utilized to process huge and complicated data that cannot be handled using traditional software for data processing.
Develop abilities to visualize data.
Companies are always searching for data scientists with specific abilities and appropriate qualifications. If you are already working as a data scientist it can be a great way to enhance your career as well as your continuous training. If you need personalized guidance in your quest to choose data science as your career path, HiCounselor can help you get started on the right track.
HiCounselor has forged technical advances to transform the hiring landscape for job seekers and recruiters alike. Our career accelerator program provides job seekers with coaching and mentorship from industry leaders employed at FAANG companies while recruiters ally with HiCounselor to empower their hiring strategy and hone in on the best candidates. Learn more about HiCounselor here and reach out to us on LinkedIn, Facebook and YouTube.
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