Instructor Feedback

Feedback from instructors already using the book

I like the book so much that I modified my course to better adhere to the contents of the book. My class is hands-on, and the book facilitates this type of learning. The case studies with available R code are the biggest bonus, but I also really like the scope of the contents and the intuitive explanations. Excellent book and ecosystem around it - this will be the single main text I’ll use.

I think the 3 strongest points of this book are

  1. Case studies with R code
  2. Intuitive explanation of many important concepts in data science.
  3. Great practice questions and data exercises.

My experience so far is as follows: I am trying to cover chapters 1-18 in about 12 weeks. We are four weeks into the fall semester, and I am finishing chapter 6, so as of now it seems doable. The extras that accompany the book (slides, quizzes, complete case studies) make my life as an instructor much easier. So far the student feedback has been positive (they find the class rewarding, although I make them work hard). It is a pleasure to teach from this book.

Prof Peter Fuleky, Associate Professor of Economics, University of Hawaii and editor of new book, Macroeconomic Forecasting in the Era of Big Data

I used the book in an introductory econometrics course in the doctoral program of a business school. I had students from marketing, management, accounting, operations research and finance. This was a class of 10 sessions, 2.5 hours each, we had time to cover the basic cross-sectional case and add in some machine learning and the basics on causality; we covered Chapters 4-11,13-14,19-21

I have been looking for a book that takes a more application-oriented approach compared to standard econometrics texts. A particular advantage of the book was the large number of worked out case studies coded in several languages (some students used Stata, some R) that really helped to drive home the intuition.

Two other features that I found very useful: the intro to machine learning methods (by now these should be part of the standard econometric toolbox) and the discussion of causality. This latter topic is crucial for doctoral students in applied areas such as a business school.

I think for an Introductory Econometrics class in business schools, the book is a perfect textbook, supplemented with a bit more advanced theoretical material on the basic econometric properties of regression estimators (consistency, asymptotic standard errors under different assumptions).

The case studies came out from their feedback as the most useful.

Prof Andras Fulop, Professor of Finance, ESSEC Business School

I used the book at the Executive MBA program. I had participants with a huge variety of background: some of them had engineering PhD with up-to-date knowledge on statistics and coding, while others had not worked with math since high school. Part I, Chapter 1-6 was used as the core for the teaching material.

Gabors Data Analysis textbook allows to use different case studies to provide examples for the basic notions in data analysis. It can be used as an excellent base to what should an executive member pay attention and ask from the employees, when reading a statistical report. The structure of the book helps to build up these main questions along with the notions, that an EMBA student must be aware of. With the help of different case studies I could not only guide the student through this process, but to give them enough practice to attain how to utilise the conclusions from such statistic based reports.

The most useful aspect was that the book allowed case study based teaching and the possibility to relate their daily business to the notions that they have studied.

Agoston Reguly, Phd in Economics candidate, CEU

Reviews from future users

I would like to wholeheartedly praise this book. I am totally impressed by its depth, clarity and applications of the book. The exercises are highly applied and coming from industry-relevant questions which will be of highly interests for economics, business, or even data science students. The Online Resources are great with clear and detailed codes and instructions in R, Stata, and Python, which provide a rich range of approaches for students. I will highly recommend this book for my students if they would like to develop the tools and understandings of econometrics and data science techniques. I would like to thanks Bekes and Kezdi for their excellent book. It’s one of the best textbook in econometrics and data science that I’ve ever read.

Dr. Canh Dang , LSE Fellow, Department of Economics, London School of Economics (UK)

I have reviewed the book, and I think it is excellent. I plan on using it as a supplemental book for my Business Strategy and Analytics module here at UCL School of Management. I find it to be quite exhaustive and very well organized. It is very consistent with how I organize my module. I think they covered nearly everything that I would want covered in a book like this.

Dr. Anil Doshi, Assistant Professor, UCL School of Management (UK)

“The structure of the book is revolutionary with respect to existing textbooks, both in terms of coverage and approach. In Part I (Data Exploration), rather than emphasising/pivoting around formal statistical procedures or properties of data generation processes, the authors focus on conceptual presentation and case studies, and yet, the intuitive concepts are rigorously framed. In Part II (Regression Analysis), I found Chapter 8 (‘Complicated Patterns and Messy Data’) particularly important to bridge the abstract, ideal configuration of regression analysis with practitioners’ problems emerging when facing actual data. Parts III and IV introduce frameworks allowing to predict (Part III) or explain (Part IV) a target variable. These latter parts evince epistemological depth in the authors’ conception of applied statistics, as well as an unrivalled, up-to-date organisation of topics, according to emerging trends in data science. Technical details are kept in “Under the hood” sections, allowing students to delve into more formal aspects of the topics presented. Finally, the distinction between ‘Practice Questions’ and ‘Data Exercises’ allows instructors to fine-tune practical sessions according to a differential emphasis on conceptual vis-a-vis hands-on aspects of module delivery.

Dr. Ariel Wirkierman, Lecturer in Economics, Goldsmiths, University of London (UK)

“Data Analysis for Business, Economics and Policy is an excellent and much needed companion for practitioners in various policy fields. It is not simply a textbook on a broad range of methods, but a hands-on guide for data analysis in practice. It guides the reader firmly through the process of actual empirical analysis, demonstrating how to answer policy and business questions through a broad set of case studies. While covering a large variety of topics, the book never loses focus of the most acute problems practitioners are likely to face. It should be definitely included in the welcome package of every junior data analyst.

Dr. G. Koltay, senior economist in the European Commission’s DG Competition Chief Economist Team.

You can see a list of programs that are using this textbook.