Many thanks to Springer and the authors for making these books free access. A massive collection of books in technology and science, 407 books in total. LINK

Of course, I didn’t download the all, and have no plan to do so. If I did, I would really not go through them. Here is a short list that I compiled and why.

My list is going to be very biased to:

  1. Topics I like
  2. Authors I like
  3. Things I want to learn, sometimes just a few chapters

From the best Trevor Hastie and Robert Tibshirani

  1. The Elements of Statistical Learning Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman

  2. An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Definitely top of the list, you can miss out the rest of 400 books. But you can not miss out these ones. I would suggest to read “The Elements of Statistical Learning” first: very easy to follow, clear definitions and illustrations. Then read “An Introduction to Statistical Learning with Applications in R”, which is kinda like a complement with somewhat of overlap. The second book has a bit more hands on R examples, which makes reading and practice very easy. Robert Tibshirani is one of my heros. His Regression Shrinkage and Selection via the Lasso is a must read for anyone who uses LASSO regression.

My favourite topic of all time

Definitely need copies for reference! Lots of overlap of topic for the Probability theory books, I prefer Achim Klenke’s layout.

  1. Probability theory by Alexandr A. Borovkov

  2. Probability theory by Achim Klenke

  3. Probability by Alan Karr (1993). This book is one of my favourite, unfortunately it is not free at the moment. Notice all authors’ first names start with “A”?!

  4. Bayesian Essentials with R by Jean-Michel Marin and Christian P. Robert. A good reference with practical examples.

AI = ML + DS

  1. Computer Vision Algorithms and Applications by Richard Szeliski

  2. Introduction to Data Science A Python Approach to Concepts, Techniques and Applications by Laura IgualSanti Seguí

It’s actually pretty hard to pick my top ones for this section. Here is another good list of 65 books are relevant to the data and Machine Learning field. LINK

Life science

Genomic studies

  1. Phylogenomics – An Introduction by Christoph Bleidorn. This is a very good introduction. Many basic biology and chemical process are explained in this book, with plenty graphical illustrations.

  1. Bioinformatics for Evolutionary Biologists – A Problems Approach by Bernhard Haubold and Angelika Börsch-Haubold. This book is more practical compare to the previous one. A good guide for a dry lab person, with lots of command line examples.

  2. Human Chromosomes by Orlando J. Miller and Eeva Therman. This book was written in 2001, before we stepped into the genomic era. A good biology text book for the human genome/chromosomes. Very enjoyable read.

  3. Applied bioinformatics – An Introduction by Paul M. Selzer, Richard J. Marhöfer and Oliver Koch. Compare to the other bioinformatics, this one has a lot of examples and links with web interfaced tools. A good practical guide for a wet lab scientist to pick up some bioinformatics skills. I hope the write another of this book, so many more topics I want to learn as well.

Clinical studies

  1. Fundamental of clinical trials by Lawrence M. Friedman, Curt D. Furberg, David L. DeMets, David M. Reboussin and Christopher B. Granger. Very good book. Topics cover from cohort selection, study design to data analysis and regulatory Issues. High level and essential, a must read!

  2. Survival analysis by David G. Kleinbaum and Mitchel Klein. A more in-depth book on survival analysis and cox model. Practical two column layout: figures and tables on the left and text on the right. A must read! I might actually get a physical copy for this book!

  3. From Ton J. Cleophas and Aeilko H. Zwinderman. The following three books are very very brief, and covers many topics and examples. Keeping them here as I want quickly flip through it.

Other books that I find useful

  1. Data Structures and Algorithms with Python by Kent D. Lee and Steve Hubbard. This is a very good starter for computer science. It has some nice illustrations and formulas to explain why.

  2. Object-Oriented Analysis, Design and Implementation by Brahma Dathan and Sarnath Ramnath. I am a C++/C/python/R user, and going learn Java with this book.

  3. LaTeX in 24 Hours by Dilip Datta. Definitely recommend this book to master and PhD student, it’s gonna save you so much time when writing thesis and Googling. Even with 13 years Latex experience, I still learn lots when I flip through the book.

One foot into financial quantitative analysis

I have so many friends made a move from a bioinformatics and finance. I just want to know what I am missing out here. :)

  1. Introduction to Time Series and Forecasting by Peter J. Brockwell and Richard A. Davis. This is an actually quite good stats textbook.

  2. Principles of Microeconomics An Integrative Approach by Martin Kolmar.

  3. Applied quantitative finance by Wolfgang Karl Härdle, Cathy Yi-Hsuan Chen and Ludger Overbeck