What is Data Science? What does a Data Scientist actually do? Well, the workflow of a Data Scientist can be explained effectively through a data chart.
- First of all, when the client approaches a Data Scientist, he tries to have a proper understanding of the said problem and its nature. This step assists the scientist to get a clear picture of the requisites for solving the problem.
- After this step, the Data Scientist tries to work on the design and scope of the problem in question. After this, the Data Scientist has to frame the problem. Following this, the scientist has to take in the data. This data can come from either the client or any external source as well. In addition, this data can be related to anything, from being associated with the client to being related to Micro Economics to being from social media channels such as Twitter or Facebook.
- After the scientist understands what kind of data he needs to work with, he then makes efforts to gather it at one single place. This is considered to be the second step in the workings of Data Science.
- This step requires the cleaning of the data and then giving it a perfect format which is user-friendly. This goes in as the third step of Data Science workings. After this, a scientist then models the present data. This step assists the scientist in having a clear picture of how does the data looks like after framing. A scientist tries to frame the model as per the given problem.
- After the model has been framed, the scientist then makes efforts to evaluate the model’s KPIs. These KPIs or the key performing indicators need to be increased for getting some monetary gains. The next step involves validating the model and making sure that there are no flaws in it and it is working absolutely fine as per the client’s requirements. However, it may seem easy and straightforward but it is actually a difficult and never ending process.
Now, as per experts, the question of what is Data Science can have an answer that it is all about asking questions. The scientist is expected to ask questions such as what, who, why, and how. After getting the answers to these questions the scientist needs to do data crafting. Afterwards, he needs to make use of a toolkit for building the model to solve the given problem.