Reading recommendations


Some nice and fun readings on data and metrics

  • Alex Edmans (2024) May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases―And What We Can Do about It. One of planned reading for summer 2024
  • Ethan Mollick (2024) Co-Intelligence: Living and Working with AI Super useful book on how we might integrate work with AI. Includes a supernatural animal.
  • Daniel Kahneman (2011) Thinking fast and slow A great book summarizing a live’s resarch of the economics Nobel-winner psychologist.
  • Michael Pollan (2008): In defense of food) A great book from an investigative journalist on what we should eat and why, with a very good description of what nutrition research can and cannot uncover using observational data.
  • Andrew Leigh (2018): Randomistas: How Radical Researchers Are Changing Our World Interesting review on experiments in business, as well as government. From an academic/politician.
  • Michael Luca and Max H. Bazerman (2020) The Power of Experiments: Decision Making in a Data-Driven World New dawn of experiments using large datasets with a focus on testing at businesses such as Airbnb or Uber.
  • Carl Bergstrom Jevin West (2021) Calling-bullshit A fantastic book based on a very famours course will help you see through deception by statistics.
  • Tim Harford (2021) Data Detective Great storytelling on how we use and feel about data and statistics. FYI, starts with a quote from the Empire Strikes Back.

Sports and data

Intro Data Science / statistics books

Data vizualization

Our book

Okay, so you have read some nice books. Why not read our book:

More advanced stuff

Advanced/techincal books on data science and prediction

Advanced/technical books on causal inference

  • Joshua Angrist and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics The book that started it all: talking about key econometrics tools in a precise yet accessible and focused way. Aimed at post-graduate economics student.
  • Scott Cunningham, 2020 The Mixtape Advanced, formal but highly accessible discussion of key tools of causal inference using examples from some great academic papers.
  • Judea Pearl The Book of Why - intermediate book on causality, with interesting stories and great care into developing theoretical structures and measurement of causal links.

Advanced book on the intersection of Econometrics and Machine Learning

Other post course book

Blogs and more

Interesting, non-technical articles

Blog posts

Podcasts, blogs to follow

Practice data and code