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Your "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
by rcar 10y ago
Your "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.