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I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and oth
by ykler 10y ago
I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and other areas of math) is that almost everyone in it feels mediocre. It's like 10% of the people are 10x better than the other 90%, 1% are 10x better than the next 9%, .1% are 10x better than the next .9%, etc. The top two people in the field seem to be notably better than everyone else. Another thing is that the problems are incredibly arcane. They have no connection to practical concerns or even often other parts of math, and also you can't really explain what you are working on to someone who works in a different area of mathematical logic (unless maybe the person is extremely good). So you have a lot of people getting PhDs who are very, very smart by any normal measure but who would be doing mediocre and arcane work in mathematical logic. Part of the appeal of working in data science or working for the NSA or something is that it is somewhat down-to-earth and people will think you are really smart even if you are at the bottom of the top .01%. (And you can do really great work at this level -- I don't mean to say it is all about caring what other people think.) It is a little weird that the blog post is so negative in a way.
- george_ciobanu 10y agoA lot of these "arcane" topics have long-term horizons. Agree with everything else but the whole point of research is to probe the space of possibilities (theoretical and practical) to find major ideas. Most of them fail but that's part of the exploratory process - it's expected.
- glial 10y agoIt's expected, but as someone doing research it's super discouraging. I can certainly see the appeal of moving away of basic research with a very long term time horizon (fine for science, but frustrating for individuals) and towards a field with the potential of more immediate impact.
- deleted 10y ago[deleted]
- rcar 10y agoYour "90/10 all the way down" explanation is a really good one that I may borrow. I often tell people new to the field that because there's more in the overall field of data science that anyone can know, everyone feels like they're deficient from time to time since an aspect you don't understand but someone else does will naturally come up. It can be useful when that happens to keep in mind the subjects that you do know well and others don't to help stave off the impostor syndrome that can crop up.
- throwaway729 10y agoI think you got the parent's meaning exactly backward. The 90/10 thing is about their phd field of study (mathematical logic), and they're saying that something almost opposite of that is true for data science.
- visarga 10y agoI think it's still true for data science. Nobody can keep up with the flood of research taking place.
- ska 10y agoWith data science there is a lot more demand for skilled people who are not at the top of the game. As a result there are a number of really good career paths, which really isn't what you see in academic work. Industry can cheerfully, usefully absorb thousands of solidly "average" (in this particular sense) mathematicians (or similar) in a way that academia just has no plan for. Even if they do not work on anything quite as technically interesting as they had previously in other ways the job may be more rewarding; And I don't mean simply financially. That being said, most academics are not a good fit initially.
- throwaway729 10y ago> Nobody can keep up with the flood of research taking place. That's very different. No one can keep up with the flood of JS frameworks, either. Information fire hoses are very different from extreme differences in ability.