3 ms·
I 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
by throwaway729 10y ago
I 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.