How do you feel about line "races"? (ex. https://datasciencetexts.com/diversions/college_ranks_race.h...)
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan is also considered pretty good.
Thanks for the feedback! ISL is indeed a good option, especially for the more application-oriented; it's on the todo list!
I think the situation has improved somewhat as visualization tools have become easier to use. We made this simple visual [1] to help people understand what they might get out of linear algebra, and it was easy enough for some statisticians to accomplish.
[1]https://datasciencetexts.com/subjects/linear_algebra.html
I think you'd be better off buying a different algorithms textbook for another perspective. The Algorithm Design Manual is a popular (and much cheaper) option.
Shameless plug: https://datasciencetexts.com/ is a list of books related to data science that you all might enjoy!
Thanks for the feedback!
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