A Reading List for Data Scientists

Colleagues and friends often ask me for book recommendations on data science. Here are seven books that teach some of the most important mental models on the limits of predictability and model building, as well as prediction techniques that actually work in practice.

  • An Introduction to Probability and Inductive Logic by Ian Hacking
  • Against the Gods: The Remarkable Story of Risk by Peter L. Bernstein
  • Superforecasting: The Art and Science of Prediction by Philip E. Tetlock and Dan Gardner
  • Predictably Irrational by Dan Ariely
  • The Wisdom of Crowds by James Surowiecki
  • Six Degrees by Duncan J. Watts
  • Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb

While there is some overlap between them, each book covers a major theme that is distinct from all the others. After Hacking, the rest of the books can be read in basically any order. Enjoy!

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