Data Science and Machine Learning Books

As we survey successful data scientists on various aspects of their careers, we are gathering a collection of the books that helped them grow the most in their profession.

The books featured in this list cover a wide variety of topics that a successful data scientist should master, including programming, machine learning, and statistics.


We thank all the data scientists who participated in our recent Data Science survey and our Data Science Interview Series for sharing the titles of the books that helped them grow in their career.

Data Science

  • Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
  • Practical Data Science with R
  • Python Data Science Handbook
  • Mining of Massive Datasets
  • Bayesian Methods for Hackers
  • Applied Predictive Modeling
  • Mastering java for data science
  • Machine Learning

  • Bayesian Reasoning and Machine Learning
  • Hands-On Machine Learning with Azure
  • Machine Learning by Mitchell
  • Machine Learning: A Probabilistic Perspective
  • The Hundred-Page Machine Learning Book
  • Statistics

  • An Introduction to Statistical Learning
  • Elements of Statistical Learning
  • Practical Statistics for Data Scientists
  • Probability and Statistics by DeGroot
  • Multivariate analysis
  • Others

  • AI – A modern Approach
  • data governance imperative
  • Ghost in the Wires: My Adventures as the World’s Most Wanted Hacker
  • Introduction to the Bootstrap
  • Playing to Win
  • The Book of Why
  • The Brief History of Time
  • Women in Data