Do You Really Need a Unicorn?

unicorn

Reposted from Data Science Central:

The world of data science is splitting into two distinct camps, the start-up app world and the commercial world.  The good news is that almost all the opportunity lies in commercial predictive analytics where you can broadly specialize and still play with all the latest innovations.

In case you’re the only person who hasn’t heard this phrase, data scientists have increasingly been referred to as ‘unicorns’ as in ‘as rare as a unicorn’.  As a data scientist I have taken exception to this since it seems to set an unrealistically high bar and simply isn’t true of the many data scientists I know personally.  (See my earlier article “How to Become a Data Scientist”). 

In February I spent several days at the Strata Conference catching up on all things analytic and big data, and yes there was still a fairly strong theme around the difficulty in finding these unicorns.  One very persuasive speaker actually spoke about how to affect the capture, which in his version was to take fresh Ph.Ds. in math, statistics, OR, or computer science and train them up himself.  Well, I thought, 1.) If the future is limited to data scientists with fresh Ph.Ds. then the supply is indeed vanishingly small, and 2.) What are the rest of us supposed to do for talent? Three things became apparent…

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