Instructor Feedback


University of Hawaii - course on forecasting

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

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.

ESSEC Business School Phd - course on econometrics

Prof Andras Fulop, Professor of Finance, ESSEC Business School

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.

CEU Executive MBA - Intro to data analysis (business statistics)

Agoston Reguly, Phd in Economics candidate, CEU

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.

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