How do you feel about line "races"? (ex. https://datasciencetexts.com/diversions/college_ranks_race.h...)

Statistical Rethinking: A Bayesian Course with Examples in R and Stan is also considered pretty good.

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

Nice site, but it's worth giving some info about yourself on the site and why I should trust your advice, given that these books are expensive.

In elementary machine learning, you give two options. You should really include introduction to statistical learning by the same folks who wrote ESL. It's a great book that covers the same ground as ESL but with less math.

Thanks for the feedback! ISL is indeed a good option, especially for the more application-oriented; it's on the todo list!

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.

Thanks sir

Shameless plug: https://datasciencetexts.com/ is a list of books related to data science that you all might enjoy!

This is fantastic!

Thanks for the feedback!

Howdy Hacker News, We’re Data Science Texts, and we’d love your feedback on our website. We’re often asked for recommendations for books on data science related topics, and we’re hoping to provide a useful starting point for other data scientists to learn new methods. We would welcome any suggestions for improvements to the website, as well as suggestions for topics or particularly good books that we’ve missed.