5 ms·
Most data scientists, particularly those coming from the cs department, lack most probability and statistics fundamentals. I doubt many of them have even heard
by x0x0 12y ago
Most data scientists, particularly those coming from the cs department, lack most probability and statistics fundamentals. I doubt many of them have even heard of an anova, or sampling distributions, F tests, chi2, etc.
To be fair, ml tends to focus very heavily on prediction, not inference / interpretation of betas. In many tree models how to even understand coefs is an open question.
- ced 12y agoanova, or sampling distributions, F tests, chi2 Most of the big ML books are heavily Bayesians, and these subjects are less discussed (though IIRC Gelman's book has a "Bayesian ANOVA"). Even Elements of Statistical Learning, which is very frequentist in its approach, only references ANOVA in passing. Do you have any book to recommend about these fundamentals?
- x0x0 12y agobasic level, very approachable, filled with case studies (and with R code to run them easily found), but stupidly expensive: _statistical sleuth_ by ramsey (but, you know, pdfs can be found on the internets) intermediate level, covers some blocking IIRC: _Statistics for Experimenters_ by Box et al advanced: I thought quite good, but classmates did not universally love. Unfortunately does not come with case studies or R code to run them; I have a bunch but (very unfortunately) printed instead of computerized and, in any case, probably copyrighted by my professors. _Experiments: Planning, Analysis, Operation_ by Wu and Hamada. The math is not complex but can be involved for various types of blocking designs.
- ced 12y agoThanks a lot, statistical sleuth looks very readable and interesting! How do you feel about the Bayesian approach to these questions? (cf. Gelman's Bayesian Data Analysis)