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Comment by ExploDron | Hacker News Reader

So this is a question for those of you in the comments.

I'm finishing up a Ph.D. in engineering (heavy into climate change research, so tons of programming + mathematical + statistical knowledge in addition to combing through TBs of data with R and other languages).

What kinds of problems are frequently present in the data science industry that differs from academic research?

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I realize this isn't a proper answer to your question, but it reminds of a tweet from Monica Rogati:

  "A decade in academia taught me a bunch of sophisticated algorithms; a decade in industry taught me when not to use them."

Source: https://twitter.com/mrogati/status/726115691703619584

Welp that's really fair. Thanks for the quote.

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