Data Science Unicorn

After students watch Dr Patil's address, the next questions I usually get are (1) What kind of job or role should I look for in the field of data science? and (2) What do I study next to learn how to become a data scientist?

Originally, the core of the data science discipline came from applied mathematics, statistics, and computer science. However, we have learned from experience that while having expertise in a single discipline is useful, there is exponential value in having expertise across disciplines. The image above is a re-creation of an increasingly common concept of the "data science unicorn." Interpreted, it means that the most valuable—and most difficult to find—data scientists are those with the complementary attributes of (1) expertise in the domain of the problems to be solved with data science (e.g., business, healthcare, natural sciences), (2) statistical expertise to understand the various analyses and their uses and roles, and (3) technical expertise to be able to deploy the findings.

The data science unicorn is what you should strive to become. But, by definition, these people are rare. In current practice, we typically see people who came from one of the three reference disciplines working together in a team. However, the more you can extend your expertise to cross all three of these domains, the greater your value will be.