that seems plausible to me, but then I wonder why more US people aren't snapping up those lucrative jobs
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about 2/3 of my cohort has, instead of academic market. but there aren't so many of us really
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Replying to @eigenrobot @sonyaellenmann and
but I mean--it takes a long time to train (i) math stats, (ii) causal inference, (iii) basic programming,
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Replying to @eigenrobot @sonyaellenmann and
(iv) data engineering, (v) forgetting trad stats + learning ML. and field not specialized yet, so
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Replying to @eigenrobot @sonyaellenmann and
at least a passing familiarity with each of these things is important. (I think? that's my read..)
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Replying to @eigenrobot @sonyaellenmann
causal inference is optional and overkill for most applied things
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Replying to @bobpoekert @sonyaellenmann
most data science is "throw a random forest at it"
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Replying to @bobpoekert @sonyaellenmann
probably my microeconometrics bias showing. tho my old employer is all about causality these days afaik
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Replying to @eigenrobot @sonyaellenmann
I think the syllabus for http://alex.smola.org/teaching/berkeley2012/ … is more or less right
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Replying to @bobpoekert @sonyaellenmann
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/ …
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