While data scientists often come from many different educational and work experience backgrounds, most should be strong in, or in an ideal case be experts in four fundamental areas. In no particular order of priority or importance, these are:
There are other skills and expertise that are highly desirable as well, but these are the primary four in my opinion. These will be referred to as the data scientist pillars for the rest of this article. In reality, people are often strong in one or two of these pillars, but usually not equally strong in all four. If you do happen to meet a data scientist that is truly an expert in all, then you’ve essentially found yourself a unicorn. Based on these pillars, a data scientist is a person who should be able to leverage existing data sources, and create new ones as needed in order to extract meaningful information and actionable insights. These insights can be used to drive business decisions and changes intended to achieve business goals. This is done through business domain expertise, effective communication and results interpretation, and utilization of any and all relevant statistical techniques, programming languages, software packages and libraries, data infrastructure, and so on.
The Data Science Process