Slightly off topic: They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. Imho universities should give a bit more attention on how to write quality software; a bit of test driven d…
This is precisely why I dread hiring academic-only profile. Note: I am myself from academia, but I was lucky enough to specialized in CS-related field (NLP). When I wanted to leave academia, Data Science was not a thing where I live and I had to start again at a junior dev position (i.e.: it was that, starving or staying in academia). Now, I work with a lot of people way smarter than I am, who are mostly useless beca…
Mathematicians becoming data scientists
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Re: Mathematicians becoming data scientists
#122Slightly off topic: They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. Imho universities should give a bit more attention on how to write quality software; a bit of test driven d…
> They still can't build proper software. Academics (including mathematicians) are notoriously bad at writhing production grade software. This leads to handovers of 'proof of concepts' to seasoned software developer team who than struggle with the (often complex) mathematics/science behind it. So you want employees who can understand complex mathematics and science but are also good software engineers. Those people e…
My theory of bell curves. Academia accepts a much wider distribution so that it can get some few at the far right. Business hates the variation as it wreaks havoc on building your brand/growth etc and wants the average just to the right of profitable.