Published by Cambridge University Press, 2021
Data analysis textbook mixing econometrics and data science
This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy.
Covers data exploration, regression, machine learning, causality
The textbook offers a complete, curated curriculum from data collection to machine learning and causal analysis:
- Data exploration: data collection and quality, tidy data and wrangling, exploratory data analysis and visualization, generalizing from data, and hypothesis testing.
- Regression analysis: non-parametric and linear models, functional form, internal and external validity, probability models and time series regressions.
- Predictive analytics: loss function, cross-validation, tree-based machine learning methods (CART, RF, boosting), classification, and forecasting from time series data.
- Causal inference: potential outcomes framework and causal maps/DAGs, experiments, matching, difference-in-differences analysis, panel data methods, synthetic control, event study.
Case studies with a global coverage on diverse topics
Working through case studies is the best way to learn data analysis. Thus, a cornerstone of this textbook are 47 case studies spreading over one-third of our material. Each of our case studies starts with a relevant question and answers it in the end, using real life data and applying the tools and methods covered in the particular chapter. Case studies cover a wide range of topics and come from different geographical areas, for example:
- Finding a good deal among hotels with multiple regression (EU) More
- Estimating gender and age differences in earnings (USA) More
- Management quality, firm size and family ownership (Mexico, International) More
- Calculating beta: returns on a company stock vs the market (USA) More
- Predicting company default with machine learning (EU) More
- Working from home and employee performance (China) More
- How does a merger between airlines affect prices? (USA) More
- Identifying successful football managers, and the effect of a change (UK) More
Endorsers: “comprehensive”, “thorough”, “accessible”, “fun to read”
- “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 (MIT)
- “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 with the tools and insights from the two fields.” David Card (University of California, Berkeley)
- “This book combines the latest techniques with practical applications, replicating the implementation side of classroom teaching that is typically missing in textbooks.” Nicholas Bloom (Stanford)
- “This sophisticatedly simple book is ideal for undergraduate or Master’s level Data Analytics courses with a broad audience.” Carter Hill (Louisiana)
- “Strong data skills are critical for modern business and economic research, and this text provides a thorough and practical guide to acquiring them.” John Van Reenen (MIT Sloan/LSE)
- “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 (Miami)
Taught in MA/BA Economics, Finance, Analytics, Business, Public Policy
This textbook was written to be a complete course in data analysis. Key type of programs/courses adopting have been:
- Graduate (MA, MSc) programs in Economics, Finance, Business Analytics / Applied Data Science, Marketing. May be taught in a single academic year.
- Undergraduate programs (BA, BSc) with a Major in Economics, Accounting and Finance, Analytics, Management. Taught partially or over two years.
- Phd in Management, Public Policy, Economics. Used as additional material for data analytical skills.
Typical course names are:
- Econometrics, Applied econometrics, Causal inference, Quantitative Methods, Research Methods, Data Analysis, Business Analytics, Applied data science
Adoption in process over 90 programs worldwide
The textbook was already adopted in over a 90 courses globally:
- USA/Canada (e.g. Columbia, U Texas, U Michgan, Penn State, Pittsburgh, SUNY, Wisconsin, Michigan, Hawaii, U of California; Simon Fraser, Alberta, Toronto, McGill)
- UK (e.g. UCL, LSE, City, Oxford, Cambridge Judge, Middlesex, Essex, Reading, Huddersfield BS, East Anglia)
- Europe (e.g. ESSEC, IESEG, Bocconi, Brescia, ULB Solvay, Heidelberg, Berlin, CEU, Vienna U,Corvinus, Antwerp, Tilburg, Amsterdam, UC Dublin, Copenhagen)
- Asia-Pacific (e.g. UWA Perth, Tokyo International U, Kyoto U. ABS Kuala Lumpur); South America (U Bogota, Insper Brazil, Monterrey Mexico)
Auxiliary material: slides, questions, exercises
- 360 practice questions – in the book (with solutions available)
- 120 data exercises – in the book
- Multiple choice questions – available from Publisher
- Slides in pdf and Overleaf – available from Publisher
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