supply of people capable of competent data work inelastic? seems like prices are adjusting, anyway . . .
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at least a passing familiarity with each of these things is important. (I think? that's my read..)
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causal inference is optional and overkill for most applied things
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most data science is "throw a random forest at it"
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probably my microeconometrics bias showing. tho my old employer is all about causality these days afaik
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I think the syllabus for http://alex.smola.org/teaching/berkeley2012/ … is more or less right
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in 2017 there would be smth in there about neural nets & recommendation would be factorization machines
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interesting. this was my first ML course, will read up on factorization machines http://www.stat.washington.edu/courses/stat535/fall14/ …
End of conversation
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