Suggest an order to read data science books I'm an undergraduate student that is currently studing data science.
However, since we generally don't delve deeply onto things in class, I want to take some time to review the fundamentals and understand things deeply in order to advance to more complex notions. When I did some research I came across these books :


Data Mining: The Textbook, by Charu C. Aggarwal
The Elements of Statistical Learning, by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Pattern Recognition And Machine Learning, by Christopher M. Bishop
Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, by Bradley Efron and Trevor Hastie


If you know or came across these books, can you please help me choose the proper order to read these books, or tell me if these is a redundant book, or even suggest a book to add to the list

Any help is appreciated, thanks.
I guess its common to start with
 "An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani" and then, if you want a deeper picture, move to
 "The Elements of Statistical Learning, by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie". 

I also really like the video course based on the first book, taught by Hastie and Tibshirani ( if im not mistaken) - a real gem in my mind. 
From my perspective its a good way to get both practical and teoretical background for machine learning.
Link to the course published at R-bloggers:
https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/