Cambridge University Press · 2021

Data Analysis for Business, Economics, and Policy

A complete course in data analysis by Gábor Békés and Gábor Kézdi: data wrangling, regression, prediction with machine learning, and causal analysis — taught through 47 case studies using real-world data, with all code in R, Python, and Stata.

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Book cover: Data Analysis for Business, Economics, and Policy
24Chapters
47Case studies
360+Practice questions
120Data exercises
200+Courses worldwide
40Countries
3Languages: R · Python · Stata

What people say

  • This exciting new text covers everything today's aspiring data scientist needs to know, managing to be comprehensive as well as accessible. Like a good confidence interval, the Gabors have got you almost completely covered!

    Joshua Angrist Professor of Economics, MIT · Nobel laureate

  • A beautiful integration of Econometrics and Data Science that provides a direct path from data collection and exploratory analysis to conventional regression modeling, then on to prediction and causal modeling. Exactly what is needed to equip the next generation of students.

    David Card UC Berkeley · Nobel laureate

  • This is an excellent book for students learning the art of modern data analytics. It combines the latest techniques with practical applications… For students looking to learn data analysis from one textbook this is a great way to proceed.

    Nicholas Bloom Professor, Stanford Economics & Graduate School of Business

  • I know of few books about data analysis and visualization that are as comprehensive, deep, practical, and current as this one; and I know of almost none that are as fun to read.

    Alberto Cairo Professor, University of Miami, School of Journalism

  • A rigorous textbook grounded in real-world learning, at once accessible and engaging to novice scholars and advanced practitioners alike. I have every confidence it will be valued by future generations.

    Kerwin K. Charles Dean, Yale School of Management

  • This is not an econometrics textbook, but a data analysis textbook. And a highly unusual one — written in plain English, based on simplified notation and full of case studies. An excellent starting point for future data analysts.

    Beata Javorcik Professor, University of Oxford; Chief Economist, EBRD

  • This is a fantastic book to have. Strong data skills are critical for modern business and economic research, and this text provides a thorough and practical guide to acquiring them. Highly recommended.

    John Van Reenen Professor, MIT Sloan & Department of Economics

  • This sophisticatedly simple book is ideal for undergraduate or Master's level Data Analytics courses with a broad audience. Using well-chosen case studies, they illustrate the techniques and discuss them patiently and thoroughly.

    Carter Hill Professor of Economics, Louisiana State University

  • In addition to the comprehensive treatment of modern econometric techniques, the book also covers the less glamorous but crucial aspects of procuring and cleaning data, and drawing useful inferences from less-than-perfect datasets.

    Laszlo Varro Chief Economist, International Energy Agency

  • Must purchase for anyone doing applied work… perfect for data scientists of all stripes.

    Scott Cunningham Author of Causal Inference: The Mixtape

More endorsements, from instructors and students too → · Instructor feedback →

Jump to key content

Instructors

Adopt and teach with the book in undergraduate or Master's programmes: slides for every chapter, course designs, solutions, and adoption examples.

Students

Learn with the book: where to start, coding setup in R, Python, or Stata, practice Q&A, and study advice.

Data & Code

Everything is reproducible: raw and clean datasets on OSF, full code for every case study on GitHub.

What the book covers

The book follows the whole process of data analysis, from collecting and cleaning data to answering real-life questions. It has four parts.

I · Data Exploration

Data collection and quality, tidy data and wrangling, exploratory analysis and visualization, generalizing from data, hypothesis testing.

II · Regression Analysis

Non-parametric and linear models, functional form, internal and external validity, probability models, time series regressions.

III · Prediction

Loss functions, cross-validation, LASSO, tree-based machine learning (CART, random forest, boosting), classification, forecasting.

IV · Causal Analysis

Potential outcomes and DAGs, experiments, matching, difference-in-differences, panel data methods, synthetic control, event studies.

More on the chapters → · Why use this book? →

Case studies: global and diverse

Each of the 47 case studies begins with a real question and ends with an answer, based on real data and the methods taught in that chapter. For example:

  • Estimating gender and age differences in earnings (USA). More
  • Management quality, firm size and family ownership (Mexico, International). More
  • Predicting company default with machine learning (EU). More
  • Working from home and employee performance (China). More
  • Identifying successful football managers, and the effect of a change (UK). More

All case studies →

More than a textbook: free courses and tools

Around the book we built courses and tools that anyone can use. You can learn to code from zero, learn to do data analysis with AI, and play with the concepts in interactive dashboards. All of it is free.

Coding courses

Learn to code from zero in R, Python, or Stata. Full open courses that take you all the way to doing the case studies yourself.

Data Analysis with AI

A full open course on doing data analysis with LLMs and agentic tools: eight sessions, assignments, case studies, and a capstone.

Interactive dashboards

Play with the concepts in your browser: eight teaching dashboards, from distributions to causal inference.

All resources

Reading suggestions and data sources, together with everything above, on one page.

The book in 100 seconds

Adopted in 200+ courses in 40 countries

In Economics, Finance, Analytics, Business, and Public Policy — from Columbia and Michigan to Bocconi, CEU, and beyond. Full list of courses →

About the authors

Gábor Békés

Gábor Békés is an Associate Professor at the Department of Economics and Business of the Central European University and director of the MS in Business Analytics program. He is a research associate at CEPR and an advising fellow at Microsoft AIEI. He has published in top economics journals on multinational firms, productivity, business clusters, and innovation spillovers, and has taught graduate-level data analysis courses since 2012.

Gábor Kézdi

Gábor Kézdi was a Research Associate Professor at the University of Michigan’s Institute for Social Research. He published in top journals in economics, statistics, and political science on household finances, health, education, demography, and ethnic disadvantage, and was co-investigator of the Health and Retirement Study in the U.S. He taught data analysis and econometrics from undergraduate to PhD level from 2002.

Gábor Békés and Gábor Kézdi at Balatonudvari, Hungary

Gábor Békés and Gábor Kézdi at Balatonudvari, Hungary (July 2018). Photo by Anna Fetter.

We could not have done this alone. Far from it. So, we are grateful, really.