Recommended books for data scientists

In this post I’d like to share some of my recommended books for learning data science and machine learning, both in theory and and practice. Fellow practitioners, let me know your favourite books or any other related resources, I’d be keen to check out some new books and add them to my list.

Theory

These are all foundational textbooks in machine learning. If you study at least one of them in depth, by which I mean formulating models, deriving and implementing the main inference algorithms, and doing the exercises. The books can be quite technical if you’re new to machine learning, but once you stick through one, you’ll find others quite accessible.

The Elements of Statistical Learning (ESL), by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
One of the classics, there’s also an online course and a new textbook accompanied by R code.

Pattern recognition and machine learning (PRML), by Christopher Bishop

Similar to ESL, this highly regarded book is another must-read.

Machine Learning: A Probabilistic Perspective, by Kevin R Murphy

If you study PRML thoroughly, you’ll be familiar with most contents in Murphy’s book. Nevertheless a fun and comprehensive book with a strong focus on principled, probabilistic approach to modelling. It also comes with code in Matlab.

Probabilistic Graphical Models, Daphne Koller and Nir Friedman

Graphical models provide a framework for representation, inference, as well as learning of probabilistic models. This powerful framework provides a unifying view to many ML models which otherwise may be viewed as just a bunch of disparate models. There’s also an online course on Coursera. 

Reinforcement learning, an introduction, by Richard S. Sutto and Andrew G. Barto

Despite still a draft, the second release is well-written and motivates the concepts and applications of RL really well.

Neural networks and deep learning, by Michael Nielsen
Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville

Michael Nielsen’s book is more hands-on and contains some cool interactive contents to aid understanding, while Goodfellow et al is more comprehensive. I recommend reading them in the given order.

*Gaussian processes for machine learning, by Carl E. Rasmussen and Christopher K. I. Williams

*The last book is on Gaussian processes, my PhD research topic. You don’t really need to know it for doing practical data science, but still a good reference. The first few chapters present the Bayesian approach to modelling and are worth reading.

Practice

Data science for business, by Foster Provost and Tom Fawcett

This book is accessible to non-technical audience like business managers. It also provides some sound principles on how to execute data science projects. Highly recommended.

Applied predictive modelling, by Kjell Johnson and Max Kuhn

Written by the author of the popular R package caret, this is a must-read for those practising data science. It contains many practical tricks and advices.

 

Data Mining Techniques: For Marketing, Sales, and Customer Relationship, by Gordon S. Linoff and Michael J. A. Berry

Don’t let the title mislead you, this is a good read on data science techniques in general, not just in CRM space.

Data preparation for data mining, by Dorian Pyle

Published in 1999 but still very relevant a day, this book can serves as a good checklist of things to inspect when preparing data for analysis.

Bandit algorithms for website optimization, by John Myles White

This book presents standard multi-armed bandit algorithms and comes with implementations in several languages.

Practical data science with R, John Mount and Nina Zumel

Not as polished as Johnson and Kuhn’s book but has few neat techniques worth knowing.