Stats and data science, easy jobs and easy mistakes

I have been writing some JavaScript, and I was thinking about how web dev / front-end people are obliged to use the very latest tools, not so much for utility as for kudos. This seems mysterious to me but then I realised: it’s because the basic job — make a website — is so easy. The only way to tell who’s really seriously in the game is by how up to date they are. Then, this is the parallel that occurred to me: statistics is hard to get right, and a beginner is found out over and over again on the simplest tasks. On the other hand, if you do a lot of big data or machine learning or both, then you might screw stuff up left, right, and centre, but you are less likely to get caught. Because…

  • nobody has the time and energy to re-run your humungous analysis
  • it’s a black box anyway*
  • you got headhunted by Uber last week

And maybe that’s one reason why there is more emphasis on having the latest shizzle in a data science job that’s more of a mixture of stats and computer science influences. I’m not taking a view that old ways are the best here, because I’m equally baffled by statisticians who refuse to learn anything new, but the lack of transparency and accountability (oh what British words!) is concerning.

* – this is not actually true, but it is the prevailing attitude

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