Reading recommendations


Books

Some nice and fun readings on data and metrics

  • 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

  • Roger Peng and Elizabeth Matsui The Art of Data Science Intro review on the steps of analyzing data.
  • David Spiegelhalter The Art of Statistics A great review of some key statistics concept from a great statitsician. A very nice introduction to any data, stats or metrics course [Recommended]

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.

Blogs and more

Interesting, non-technical articles

Blog posts

Podcasts, blogs to follow

Practice data and code