This course covers some computing aspects that statisticians should be aware of (e.g. random number generation), some common algorithms not presented in previous course work (e.g. Markov Chain Monte Carlo) as well as an introduction to statistical learning techniques such as bootstrapping, jackknifing, LASSO and tree based algorithms.
The book(s) we use for the course are the two volume set of books by Trevor Hastie, Robert Tibshirani, and others. They have graciously posted a PDF copy of the books on-line, but they are also available from Amazon at a very reasonable cost. The ISL is more of a “how-to” guide while the ESL gets deeper into the theory.
Introduction to Statistical Learning - PDF, Amazon
Elements of Statistical Learning - PDF, Amazon
My notes for this course are freely available at: https://dereksonderegger.github.io/578/
I also have a GitHub repository for R functions that I use in this course: https://github.com/dereksonderegger/sta578
Finally, here is also a really nice set of videos that the two senior authors put together. You can find them in a nicely organized list at the following RBloggers site.