My stand on learning data science is known: I think that learning “data science” as a career move is a mistake. You may read this long rant of mine to learn why I think so. This doesn’t mean that I think that studying data science, in general, is a waste of time.
Let me explain this confusion. Take this blogger for example https://thegirlyscientist.com/. As of this writing, “thegirlyscientst” has only two posts: “Is my finance degree useless?” and “How in the world do I learn data science?“. This person (whom I don’t know) seems to be a perfect example of someone may learn data science tools to solve problems in their professional domain. This is exactly how my professional career evolved, and I consider myself very lucky about that. I’m a strong believer that successful data scientists outside the academia should evolve either from domain knowledge to data skills or from statistical/CS knowledge to domain-specific skills. Learning “data science” as a collection of short courses, without deep knowledge in some domain, is in my opinion, a waste of time. I’m constantly doubting myself with this respect but I haven’t seen enough evidence to change my mind. If you think I miss some point, please correct me.
Boris Gorelik 
You’re generally right I believe, haphazardly focusing on data science generally without a context of problems seems unwise.
But a counter argument is that academic categories seem to be always in flux:
* Erich Lehmann describes the history of statistics at Berkeley https://goo.gl/h8e2bz. There really wasn’t a department until 1955.
* When I applied to college, Stanford didn’t have a CS department. Undergrads interested in AI were encouraged to apply as Linguistics majors.
* I see that many schools are offering data science degrees. Like Columbia http://datascience.columbia.edu/master-of-science-in-data-science. Who wouldn’t want to work with David Blei? http://www.cs.columbia.edu/~blei/
* I remember that once in the ’80s Boston University was offering a program in neural computing. I suspect the grads did ok.
On the academic side, maybe it ultimately depends on who is paying for the degree, whether the people managing the program are good teachers and practitioners who can impart useful insights and skills needed to thrive. Who knows, data science might eat statistics in twenty years.
If you’re a data scientist at heart, maybe you’ll just run the numbers and pick the best option for you? Taking a Bayesian approach may not be bad, most decisions in life can be reversed.
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