"Data Smart" by Foreman

"Intro to Statistical Learning" by James, Witten, Hastie, Tibshirani

"Elements of Statistical Learning" by Hastie, Tibshirani

The first emphasizes the application of basic concepts to the practice of data science, mostly using english and excel. The second is more mathematical yet quite applied, again focusing on DS tasks, but using R. The third is more mathematical yet, yet very much in the same model as the second. The second and third are free ebooks.

David MacKay: Information Theory, Inference, and Learning Algorithms

Kevin Murphy: Machine Learning: a probabilistic perspective.

Christopher Bishop: Pattern Recognition and Macine Learning

These three are all excellent, but I'm not sure they pair well with the OP's book, which is written purely from the frequentist perspective. The books practically speak different language.

Allen Downey offers many books for free [0] including a few on statistics and data science.

[0] http://greenteapress.com/wp/

Thanks!

It depends on what you want to do.

Suggest some.