From what I'm aware most people working on data science these days come from very different backgrounds (e.g. statistics, engineering, computer science, economics). Since there weren't any BSc or MSc programs on data science in the past, most candidates were for the most part self taught, as far as I know.

I was wondering which background would be the best for a data scientist?

closed as primarily opinion-based by Kiritee Gak, tuomastik, Stephen Rauch, oW_, Spacedman Sep 14 at 22:20

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    The answers to this question will be opinions. My opinion is that a combination of statistics and computer science (especially, AI) is good enough to become a DS. – nbro Sep 14 at 12:28
  • @nbro yes, I'm aware that there is no definite answer. I was wondering what would it be easier, for a statistician to become more technical adapt, or for a computer scientist to establish the necessary theoretical background in statistics? – kfn95 Sep 14 at 12:34
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    It will always depend. Maybe some statistician already have some CS/programming background, whereas others do not have it. A similar thing can be said about CS. – nbro Sep 14 at 12:36
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    Definitely put a plug in for mathematics, with a good background in statistics and programming (programming, as distinct from computer science - see Joel Spolsky for some of the key differences). – Adrian Keister Sep 14 at 12:50
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    Data science is really in its infancy industry wise, so there is a lot of room for diverse backgrounds. Think the tech industry 30 years ago. The key attributes of a data science candidate today, imho, are engagement with the field (do you understand data at a visceral level), can you do math (not just arithmetic), can you apply math to data, have business common sense, and genuine curiosity. Everything else can be mentored/learned. That said, all our hires have STEM backgrounds. – davmor Sep 14 at 13:57

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