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How the Scientist Differs from the Analyst

A data analyst deals with many of the same activities but the leadership component is a bit different. Let’s take a look at a few examples:

  1. Normally a data scientist is expected to formulate the questions that will help a business and then proceed in solving them, while a data analyst is given questions by the business team and pursues a solution with that guidance.

  2. Both roles are expected to write queries, work with engineering teams to source the right data, perform data munging (getting data into the right format, convenient for analysis/interpretation) and derive information from data. However, in most cases a data analyst is not expected to build statistical models or be hands-on in machine learning and advanced programming. Instead, a data analyst typically works on simpler structured SQL or similar databases or with other BI tools/packages.

  3. The data scientist role also calls for strong data visualization skills and the ability to convert data into a business story. A data analyst is normally not expected to transform data and analysis into a business scenario and roadmap.



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