Teaching Guide

How to use this textbook – example sequences

The textbook may be taught at MBA, MA Economics (non-PhD track), MSc in Business Economics/Management, MA in Public Policy, PhD in Management and comparable programs. It also a natural fit in Business Analytics graduate programs.

The textbook is made up of four parts, each with six chapters and an additional short essay. The whole book can be taught over 24–30 weeks in a year-long course. However, several cuts can be made to suit various courses.

Example sequences and ideas

Graduate applied Economics or Quantitative social science program

Programs: Economics, Applied economics, Quantitative Social Science, Economics with Data Science

Duration: 2 semesters

Emphasis: Offering methods a data analysist may need

Sequence: The whole textbook: Part I and Part II and Part III and Part IV. To fit into a year, advanced sections may be cut from the material or some time series chapters used in a separate course.

Undergraduate applied Economics or Quantitative social science program

Programs: Economics, Applied economics, Quantitative Social Science, Economics with Data Science

Duration: 4 semesters

Emphasis: Offering methods a data analysist may need

We believe the textbook may be applied in undergarduate programs with majors in many fields such as Economics, Finace, Quantitative Social Science, Politics, Philosophy and Economics, Business and Management. The content should be applicable easily. Case studies are already a mix of applications, and of course, different programs may use our datasets to offer additional case studies. For example, financial data used in Chapter 12 may be used in other contexts, management and sport data used in Chapters 3, 20, 21, 24 could also be used elsewehere. World Bank data we used in Chapter 2 and 23 could be easily extended with additional variables.

Sequence: The whole textbook: Part I and Part II and Part III and Part IV, with some heavier sectiosn omitted.

The key difference would be spreading out the material over two or three years, covering Part I and Part II in the first two years, with the advanced material left for the 3rd and 4th years, maybe as elective. The key difference to a graduate course should be the speed - more time shall be given students to discover tools. Also, Under the Hood derivations should be included when possible to help transition to graduate / Phd programs.

Many undergraduate programs offers a range of existing statistics and econometrics courses. How can this book fit in? Let us offer two options.

First, instructors may keep the existing set of courses but add case studies from this book. Another option is to add a new course on applied data analysis in the 3rd / 4th year building on existing foundations. A possible set of case studies would use those in Chapter 3 and 4 for exploratory data analysis and data vizualization and 7-12 for regression analysis. More advanced courses would also use the case studies in Chapters 21-23.

Instructors may still use bits on thery from the textbook to convey a data driven world view. For instance, we believe our approach to regression analysis as well as introducing potential outcome compatible DAGS for causal analysis could be rather useful.

A second option is more radical and could be a solution for programs that under structural transformation. Here one option could be keeping mathemathics in the first year, but starting data analysis early on, and teaching a great of the textbook in the first two years. At the second part of undergrdauet studies advanced courses could build on analytical and data management foundations provided by our book. For instance, in Economics, advanced econometrics courses would focus on formal presentation of some material, advanced methods, time series econoemtrics, bayesian analysis, and more.

Introductory course in a graduate social science program:

Programs: Public Policy, Sociology, Economic Policy, International relations

Duration: One 10-12 week semester

Emphasis: Introductory methods from data collection to testing, regression analysis.

Sequence: Part I and Part II Chapters 1–10 with some cuts from heavier material in chapters 5 and 6. Can be the core part of a program with other chapters as an elective.

One or two-year Graduate applied, policy program:

Programs: Public policy, applied social sciences, Econmic Policy

Duration: 10-15 weeks

Emphasis: Focus on policy applications, regression analysis to causal inference

Sequence: Refresher course on Part I and do Part II and Part IV , with maybe some cuts from chapters 1–4. With more advanced students Part II could be covered relatively fast, while students can spend more time on Part IV and read some reference paper.

Note: The course may be followed for interested students by advanced metrics course with a focus on IV, RDD, etc.

Two-year graduate economics program, non-Phd track.

Programs: Economics, Applied Economics

Duration: 2 semesters (20-24 weeks)

Emphasis: Complete coverage

Sequence: Full coverage including the Under the Hood sections to establish theory background for students taking advanced econometrics courses in the second year.

Notes: Second year students, especially those set to continue in a PhD program, can have econometrics courses focusing on advanced topics like likelihood estimation, RDD, IV estimation, discrete choice.

Business Analytics/Applied Data Science type professional programs:

Programs: Business Statistics/Management Science/Intro to Data Science in a Business/management program:

Duration: 15-24 weeks depending on exact coverage

Emphasis: Full introductory and intermediate data analysis, with some advance features.

Sequence: Part I and Part II and Part III as core; Part IV as elective.

Core data course in a graduate Business/management program:

Programs: Business Statistics/Management Science/Intro to Data Science in a Business/management program:

Duration: One 10-12 week semester

Emphasis: Introduction to key methods and overview of extensions

Sequence: Part I and Part II with some cuts from heavier material in chapters 5 and 6. + Chapter 13 to give an idea on prediction and + Chapter 20 to give an idea on experiments.

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.