I can't help but wonder about those recommender systems. With so little material on statistics I have to assume it's only about observational data, which is the best way to make the millionth+1 useless recommendation engine.
And why is it that the "data science" books never discuss DoE?
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.
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?
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.
How do you feel about the Bayesian approach to these questions? (cf. Gelman's Bayesian Data Analysis)