Data Science Summer Reading List 2016

Superforecasting: The Art and Science of Prediction by Philip E. Tetloc

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies by John D. Kelleher

Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach

Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis

Machine Learning for Evolution Strategies by Oliver Kramer

Essential Algorithms: A Practical Approach to Computer Algorithms by Rod Stephens

The Algorithm Design Manual by Steven Skiena

How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg

Assessing and Improving Prediction and Classification by Timothy Masters

Applied Predictive Modeling by Max Kuhn

Bayesian Data Analysis by Andrew Gelman

All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie

Causal Inference in Statistics: A Primer by Judea Pearl