In addition to the classics, of Introduction to Statistical Learning in R and Elements of Statistical Learning, I also recommend the newer entry from Hastie, Computer Age Statistical Inference. I haven't finished CASI -- only read a few random chapters -- but I really like how it is laid out, with focus on not just the math, but also the history. It's a great way to introduce some of the statistics in data science and help explain how the field has grown into what it is today.
If you are not 100% focused on using R and open to learning through Python, I also highly recommend the Allen Downey books Think Stats 2 and Think Bayes. They are well written and favor teaching through code instead of just math, which was really helpful for me.
Lastly, I thoroughly enjoyed Machine Learning for Hackers and its corresponding GitHub repo. It's a whirlwind tour of the most common/basic algorithms used in data science (outside of deep learning) and is focused more on making sure you understand the high-level concepts and how to use them than making sure you understand the math. In that regard, it's a great companion book to ISLR/ESL.