Qualities of good data scientist can be simplified into BOTS
B – Breaking down of a problem
O – Observation
T- Technical proficiency
S – Storytelling
- Breaking down of a problem: A data scientist should understand the problem very clearly and should be able to create sub questions from a given main problem. For example, the main problem can be to increase the sales of the music albums. The sub questions can be what factors are affecting cost, which product has greatest risk, what is correlation between length of track and sales. All these questions should be clearly stated and addressed.
- Observation includes identification of patterns in data and any outliers if present. Data Scientist should know the data in and out so that it would be helpful for solving the problem.
- Technical proficiency: A data scientist should know which tools to use and for what purpose. For example, Tableau is good for visualizations and R is good for statistics. Using the right tools saves the project time.
- Storytelling: The data analyzed, and results obtained from the model should be communicated effectively so that all the stakeholders can understand it and can be helpful for senior management to make decisions. Technical jargon should be simplified and presented so that a layman can also understand it.
By: Parth Parekh