The Data Science Association presents the Data Science Summer Reading List for 2016:
- The Master Algorithm: How the Quest for the Ultimate Learning Machi... by Pedro Domingos
- Superforecasting: The Art and Science of Prediction by Philip E. Tetloc
- Fundamentals of Machine Learning for Predictive Data Analytics: Alg... by John D. Kelleher
- Machine Learning: The Art and Science of Algorithms that Make Sense... 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 P... by Trevor Hastie
- Causal Inference in Statistics: A Primer by Judea Pearl
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