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munichong
5y

The gap of data science in industry and academia is so large. As a data scientist in a large financial company, I see that people are still using traditional models such as linear regression and SVM, while people in academia keep inventing new concepts and techniques such as deep learning.

I am not saying that we should completely embrace deep learning, or stick to classic methods. But I just feel so surprised that the gap is so large...Sometimes I am even thinking whether I am doing the right "data science"...

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machine learning

data science

career
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  • 2
    5y
    The answer is money: academia is paid to try new things, industry is paid for results. There are some big corporations who invest in research too but those are just a few by sector. Academia is great, but it's too risky and costly for the industry to go with the flow all the time. At least that's what I've observed.
  • 1
    5y
    The thing is, nobody wants to gamble billions on a hype. That's what risk loans and venture capital are for. But these investors know the risk and accept it.
  • 1
    5y
    If linear regression and SVM can produce good result, then why should we use deep learning that is computationally expensive?
    Still, if the traditional models perform bad, then I think we should try newer models like NN.
    I always start with simple model first.
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