Reading recommendations

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Books

Popular but high quality books on data

• Nate Silver (2012): The signal and the noise A great book from a leading expert in polling and sports statistics on prediction. A big picture including statistical and other kinds of predictions; a must read for all who want to do predictive analytics. [Recommended]
• Hans Rosling et al. (2018), Factfulness: Ten Reasons We’re Wrong About the World–and Why Things Are Better Than You Think) A book summarizing decades of public advocacy from the late doctor and epidemilogist Hans Rosling and his collaborators to understand the world around us by making sense of cross-country data. A must read for everyone, really.
• David Salsburg Lady tasting Tea - How Statistics revolutionized science in the twentieth century (2002) A great book about the history of statistics and statistical ideas with many great stories. A must read for statistics nerds.
• Philip Tetlock, Superforecasting: The Art and Science of Prediction A great book summarizing some of the research and ideas of one of the leading experst in prediction. Not explicitely about statistical predictions; more of a big picture reading for those who want to evaluate predictions.
• Nassim Nicholas Taleb: Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets A fascinating walk through of examples discussing the role of luck and human decision making. Shows how really really important randomness is.
• Seth Stephens-Davidowitz (2018) Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are So you think you can use data to answer some important questions? Watch out people often hide stuff. Look at what they do, not what they say. An amazing collection of stories.
• Cathy O’Neil, Weapons of Math Destruction A more sceptic take on the role of algorithms
• Albert-László Barabási (2014), Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life - a great intro into networks
• Gary Smith (2016) Standard Deviations: Flawed Assumptions, Tortured Data and Other Ways to Lie with Statistics looks interesting.
• David Spiegelhalter (2024) The Art of Uncertainty: How to Navigate Chance, Ignorance, Risk and Luck The follow-up to The Art of Statistics, on probability, luck and what we do not know. Includes how much of football comes down to luck.
• Hannah Fry (2018) Hello World: Being Human in the Age of Algorithms Algorithms in medicine, justice, cars and crime, explained with great stories and some of the maths behind them. I love Hannah Fry.
• Chris Wiggins and Matthew L. Jones (2023) How Data Happened: A History from the Age of Reason to the Age of Algorithms Looks interesting – history of data and its technical, political, and ethical impact.

Books related to decison-making with data

• Alex Edmans (2024) May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases―And What We Can Do about It. maycontainlies.com 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.
• John A. List (2022) The Voltage Effect: How to Make Good Ideas Great and Great Ideas Scale Why results from a pilot or an experiment so often fade when rolled out at scale. Pairs well with Randomistas and The Power of Experiments.
• Eric Topol (2025) Super Agers: An Evidence-Based Approach to Longevity What the evidence says, and does not say, about living longer and healthier. A good exercise in separating solid studies from hype.

Sports and data

• Stefan Szymanski Money and Soccer and Simon Kuper and Stefan Szymanski Socceronomics Two great books on understanding football via data.
• Ian Graham (2024) How to Win the Premier League: The Inside Story of Football’s Data Revolution Liverpool’s Director of Research 2012-2023 on how data shaped decisions from hiring Klopp to signing Salah. Also on what empty stadiums taught us about home advantage.
• Michael Lewis Moneyball Super famous book on how finding mispriced players can give an edge is baseball.
• Chris Anderson and David Sally (2013) The Numbers Game: Why Everything You Know About Soccer Is Wrong I just realized on of the chapters inspired a case study in our book. If you know which one, DM me, and we’ll get a drink sometime.
• Yves Dominicy and Christophe Ley (eds) (2023) What We Can Learn from Sports Data - nice collection on sport analytics.
• Ben Lindbergh and Sam Miller (2017) The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team - suggested by a dear reader, I’m excited to have a look.

Intro Data Science / statistics books

• Andrij Burkov: The 100 page Machine Learning book A popular and concise review
• 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]
• Ian Hacking (1990) The Taming of Chance Okay, I have not read it. But it promises to take you over the eremergence of statistics, starting in the 19th centrury. Suggested by a dear reader.
• Elena Llaudet and Kosuke Imai (2022) Data Analysis for Social Science: A Friendly and Practical Introduction Concepts and skills indeed
• Ethan Bueno de Mesquita and Anthony Fowler (2021) Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis Nice intro to data science, non-technical focusing on ideas such as what is a relationship (in data…)
• Andrew Gelman, Jennifer Hill and Aki Vehtari (2020) Regression and Other Stories Regression taught through real examples, with a lot of care for what the numbers mean. Code and data online.
• Rohan Alexander (2023) Telling Stories with Data The whole workflow from getting data to writing it up, with R code. Free online.

Data vizualization

• Cole Nussbaumer Knaflic: Storytelling with Data
• Edward Tufte (1983, 2nd ed. 2001) The Visual Display of Quantitative Information The classic. Data-ink, chartjunk, and many beautiful graphs.
• Kieran Healy Data Visualization - A practical introduction A second edition is announced for 2027.
• Claus Wilke Data vizualization
• Alberto Cario How Charts Lie: Getting Smarter about Visual Information
• Jonathan Schwabish (2021) Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks – practical guide to dataviz tools; more advanced.
• Alberto Cairo (2023) The Art of Insight: How Great Visualization Designers Think Conversations with leading designers on how they choose what to show and how.
• Online and in print – The Economist Graphic Detail - amazing resource with really good explanations

Our book

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

• Gábor Békés and Gábor Kézdi (2021) Data Analysis for Business, Economics, and Policy :-)

More advanced stuff

Advanced/techincal books on data science and prediction

• Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani An Introduction to Statistical Learning The standard introduction to machine learning, with an R (2021) and a Python (2023) edition. Both free online.
• Ajay Agrawal, Joshua Gans and Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence
• Ajay Agrawal, Joshua Gans and Avi Goldfarb (2022) Power and Prediction: The Disruptive Economics of Artificial Intelligence The sequel: what happens when AI predictions start to change who makes decisions.
• Eric Siegel Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
• Bradley Efron and Trevor Hastie , 2016 Computer Age Statistical Inference - intense stats book with Big Data in mind
• Christopher M. Bishop Pattern Recognition and Machine Learning
• Nina Zumel and John Mount Practical Data Science with R – nice collection of coding/analysis ideas - often related to our book.
• Hyndman and Athananasopoulos, 2021 Forecasting: Principles and Practice – useful time series book, matching how we think about time series, a good way to continue. Now also in Python: Forecasting: Principles and Practice, the Pythonic Way, with new chapters on neural networks and foundation models. Both free online.
• Kelleher, John D. Brendan Tierney, 2018 Data Science
• Sarah Guido and Andreas Müller, 2016 Introduction to Machine Learning with Python: A Guide for Data Scientists

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.
• Joshua Angrist and Jörn-Steffen Pischke (2014) Mastering ‘Metrics: The Path from Cause to Effect The accessible sibling of Mostly Harmless, for undergraduates. Five tools, lots of examples, some kung fu.
• Miguel Hernán and James Robins Causal Inference: What If Causal inference from epidemiology, with a careful treatment of target trials and time-varying treatments. Free online.
• 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

• Martin Huber, 2023 Causal Analysis – Impact Evaluation and Causal Machine Learning with Applications in R a great new accessible yet advanced book, with yes, applications. Includes application with Machine Learning.
• Felix Chan and László Mátyás edited a great collection (2022) Econometrics with Machine Learning on well, how machine learning infused causal methods for a variety of areas such as policy evulation or development studies.
• Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler and Vasilis Syrgkanis Applied Causal Inference Powered by ML and AI Double/debiased machine learning and more, from the people who developed it. With R and Python notebooks, free online.

Other post course book

• Marc F. Bellemare (2022) Doing Economics: What You Should Have Learned in Grad School—But Didn’t title has it. How to extend what you learnt, especially for Economics students. I know folks who read it on an airpline ride to job interview.
• Lionel Page (2023) Optimally Irrational has data yes, but also a lot of economics. I’m yet to read it, but like Lionel’s work a lot. If you liked Kahnemann and Tversky, you will like this too.
• Matthew Gentzkow and Jesse M. Shapiro (2014) Code and Data for the Social Sciences: A Practitioner’s Guide Short and free. How to organise code and data so your project still works a year later.
• Ron Kohavi, Diane Tang and Ya Xu (2020) Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing How A/B testing is done at large tech firms, by people who ran it at Microsoft, Google and LinkedIn.

Blogs and more

Articles, blog posts, podcasts and data sources are on a separate page: Blogs and more.