Teaching Guide

The book allows for individual learning, facilitating flexible teaching

The textook is suitable for individual learning as well as traditional classroom teaching, online teaching, or any hybrid version. It aims to include everything students need for individual learning, including intuitive explanations, practical advise, practice questions, and the opportunity to work with code. All of this helps instructors, too, to design their courses in flexible ways. In particular, they can assign entire chapters, or parts of chapters, as individual reading. That allows them spending precious contact time on the especially important or interesting topics and using teaching methods other than lecturing (e.g. going over case studies together with the students). The additional resources help instructors: they can assign homeworks or adminster quizzes based on the practice questions, and they can make students replicate the case study results, also with small modifications, in homework assignments.

Instructors can use the entire book or parts of it

The book is designed to teach a year-long course in data analysis. An academic year is approximately 30 weeks long, and the book has four parts with 24 chapters in total. Most chapters can be covered within a week, leaving extra time for some of the chatpers and review. Which parts to teach when is more straightforward for programs structured in in semesters. In programs that divide the 30 weeks into 3 parts (trimesters or quarters) it’s a bit more complicated, but there are several options. One is to teach two of the four parts together in one term, e.g., parts I and II in the first term. This can be done by allocating more time per week and/or assigning some chapters as individual reading.

Alternatively, instructors can use parts of the book to augment their current teaching.

One option is to take some of our case studies without adopting the textbook. Reading and assigning the textbook makes sense even in that case. 1/3 of the book is about the case studies, with motivation, data description, explanations, and the results themselves. Even if you opt to use our case studies only, PLEASE GIVE US DUE CREDIT, citing our textbook on your slides and handouts.

Other options include or introducing new courses based on the textbook (e.g., a course on prediction) or re-desiging existing courses with the textbook.

Designed for graduate programs, suitable for undergraduate programs

The textbook is comprehensive in that no prior knowledge of maths or stats is required beyond high school. No programming experience is needed, either, because students can learn coding during the course. The case studies cover a wide range of fields that can cater to the diverse interests of an undergraduate group. For these reasons the book is adequate for both graduate and undergraduate courses.

At the same time, this textbook is long, and learning the material requires a lot of individual work from the students. According to our experience teaching with the book, students spend at least twice as much time on individual work than on contact hours. (With three hours – 2x90 mins – of lecture and one hour – 1x60 mins – of practice session for contact time, this workload can add up to 4+8=12 hours per week.) Most undergraduate programs can’t require that much work from their students.

Thus, our textbook can be used in graduate programs as it is. Undergraduate teaching is best done by covering things slower. Parts I and II would be the subjects in one academic year, and parts III and IV, or subsets of them, may be covered in subsequent courses.

Applied approach that may also be used in academic programs

Our approach is applied. Our goal is to make students acquire a working knowledge of the entire process of data analysis with the most important methods. In particular, our textbook prepares students to carry out real-life data analysis without taking more courses. The textbook is thus well suited for masters’ level programs, such as MBA, MA Economics (non-PhD track), MSc in Business Economics/Management, MA in Public Policy, MSc in Finance, MA in Health Policy, MA in Quantitative Social Sciences, or Business Analytics programs. It can be used in undergraduate programs with a focus on business, economics, finance, quantitative social studies, or public policy. Moreover, it is well suited for academic programs that are less focused on formal statistical and econometric theory, such as Ph.D. in Management or Ph.D. in Public policy.

In addition, we think that academic programs with a formal statistics / econometrics / data science focus can also use our textbook in their more applied data analysis courses.

Most such programs start with formal courses in statistical and econometric theory and move on to applied empirical methods courses in their 2nd year. Our textbook can be used in those applied courses, perhaps together with other textbooks.

However, econ PhD programs could teach their empirical methods in an alternative order, too. They could start with more applied data analysis and move on to more formal and advanced topics later. Our textbook is suitable for such teaching. It doesn’t build on previous formal knowledge, but it explains the whys and hows in detail. Indeed, starting with our textbook may be the better option in many programs. According to our experience, students appreciate if their first data analysis courses equip them with skills and knowledge so they can start working on actual empirical projects. Moreover, applied courses tend to increase students’ motivation to learn more, taking more advanced and more formal statistics, econometrics, and predictive analysis / machine learning courses later.

A few tips

Quizzes. Instructors can administer a quick quiz at the beginning of each lecture, using practice questions from the most recently covered material. Such quizzes make students more likely to review the material before class and use the practice questions to check their knowledge. They alse serve as an attendance check and make sure students arrive on time.

Assign reading in advance. The textbook is suited for individual learning. Instructors can make active use of that feature and assign readings to students. Indeed, they can assign an entire chapter in advance, do simple quizzes in the beginning of class to make sure students study the material, and focus on questions and case studies.

Run code in class. Instructors can reproduce all case study results by running code, in front of the class or together with students, in both online and offline settings. According to our experience, students appreciate seeing the sausage in the making, and it empowers them to tinker with and run code on their own. In a traditional setting with lectures and practice sessions, the practice sessions instructors should always run code in class. But lectures can include demostrations running code, too.

Make students replicate results. Each chapter ends with five data expercises, some of which are simple variations on the main case study. Students should start with the case study code and modify them to get the new results. According to our experience, students like replicating the results and doing simple modifications, because this helps them with coding as well as understanding the concenptual aspects of data analysis.

Example sequences

We list a few examples of how our textbook can be used in particular programs. These examples assume using our textbook for all, or most, of the material that the courses want to cover. If, instead, instructors want to use parts of our textbook, such us our case studies, they can do that in any way they like in statistics / econometrics / data analysis courses in any program.

Graduate applied business, economics, or social science programs

Program examples: MA Economics (non-PhD track), MA Applied Economics, MA Quantitative Social Science, MSc in Management Science, MSc in Finance, MA in Public Policy, MA in Health Policy

Duration: 1 academic year (2 semesters / 3 trimesters)

Sequence: The entire textbook. I Data Exploration, II Regression Analysis, III Prediction, and IV Causal Analysis.

Graduate business analytics programs

Program examples: MSc in Business Analytics, MSc in Data Science for Business, MSc in Business Statistics

Duration: 1 academic year (2 semesters / 3 trimesters)

Sequence: The first three parts of textbook: I Data Exploration, II Regression Analysis, III Prediction, perhaps addint the first two chapters in part IV Causal Analysis (the framework and running experiments).

Undergraduate programs focusing on economics or quantitative social sciences

Program examples: 4-year US-style undegraduate programs with econ, social science or business major; 3-year Eurioean-style specialized undergraduate programs in econ or quantitative social sciences

Duration: 1 academic year (2 semesters / 3 trimesters);

Sequence: The first two parts of the textbook. I Data Exploration, II Regression Analysis.

In subsequent courses, the second half of the book may be used, too: III Prediction and IV Causal Analysis.

Business, policy, or social science PhD programs, first year

Program examples: PhD in Management, PhD in Finance, PhD in Public Policy

Duration: 1 academic year (2 semesters / 3 trimesters)

Sequence: The entire textbook: I Data Exploration, II Regression Analysis, III Prediction, and IV Causal Analysis.

PhD in economics, entire first year sequence of statistics and econometrics

Duration: 1 academic year (2 semesters / 3 trimesters)

Sequence: The entire textbook: Part I and Part II and Part III and Part IV.

PhD in economics, second-year applied econometrics / data analysis courses

Duration: 1 academic year (2 semesters / 3 trimesters)

Sequence: The second part of the textbook. Part III Prediction may be complemented with other data science / forecasting books (Hastie and Tibshirani, Hyndman et al). Part IV Causal Analysis may be complemented with more advanced causal analysis books (Angrist and Pischke, Imbens).

A single core data course in graduate business/management programs

Duration: one semester

Sequence: I Data Exploration and II Regression Analysis. + It may make sense to cut from the heavier material in chapters 5 and 6 and, instead, devote time to the framework of prediction (Chapter 13 and designing experiments Chapter 20).

FAQ for instructors

Questions and answers for a variety of course and program types

Early adopters

You can see a list of programs that are using this textbook. We’ll soon report some feedback.