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.

This book was either the assigned textbook or recommended reading in every Masters program I researched. Due to its advanced nature, you'll find that book #5 in this list -- An Introduction to Statistical Learning with Applications in R (ISLR) -- was written as a more accessible version, and even includes exercises in R. It's usually recommended for beginners in data science to master the content in ISLR before moving to ESL, where you'll get a more theoretical background. Just mastering ISLR is often enough for data analyst roles. Overall, ESL takes an applied, frequentist approach, as opposed to a Bayesian approach like in the next book. Exercises in this book are not only challenging, but also very useful for individuals generally interested in machine learning research.

For books added in 2016, click here. And if you are interested to see what kind of books were published just 5 years ago, click here: you will see that hot topics are changing over time, with less data mining, more deep learning and Python, just to give an example. You can also use the search box (on the top right corner on any DSC webpage) to find books on a specific topic, such as books on deep learning.