Last edited: January 21, 2026

Economics in the Age of Big Data

Course: Econ 337

Instructor: Enes Işık

Email: eisik[at]umass.edu

Venue: MoWe 17:30 - 18:45 at Machmer Hall room E-33

Office hours: Tuesdays 17:30-19:00 or by appointment

Textbook: Not required. Course materials will be provided by the instructor. ****

Semester: Jan 29, 2026-May 8, 2026, Number of class meetings: 24

Final Grades Due ****by 11:59 p.m., May 17

Course Description and Goals

This course aims to equip students with the conceptual, analytical, and practical tools needed to navigate, evaluate, and produce knowledge in the data-rich social sciences, with a particular emphasis on improving the skills necessary for informed and active democratic engagement, skills that will ultimately help them make sense of the joke shown on the right.

by Randall Munroe, xkcd

We begin by examining the foundations of knowledge, i.e., how scientists and social researchers reason (sometimes differently), what counts as expertise, and how ideology shapes inquiry so that students can better understand how claims about the world are constructed and contested in public debate.
We then develop students’ capacity to ****critically evaluate quantitative evidence, interpret numerical and visual information, and distinguish correlation from causation, enabling them to assess policy arguments and media claims with clarity and skepticism. Building on this epistemic groundwork, the course explores research credibility, replication, and the role of emerging technologies such as large language models, highlighting how evidence quality influences democratic discourse.
In the final part, students apply these skills to major public issues, inequality, debt, climate change, political conflict, investment, cooperation, and gender dynamics, while gaining hands-on competencies in R for reproducible data analysis.
By the end of the course, students should be able to explore empirical claims responsibly, communicate evidence clearly, and participate more effectively in democratic discussions where data and expertise influence collective decisions.

Students are expected to complete all required readings before each class session, and a brief quiz on the assigned material will be given at the start of class.
After Week 7, students will take an in-class midterm exam covering the content from parts I and II of the course.
With a peer, students will present selected research papers, apply their critical evaluation skills to these papers.
Three problem sets will be assigned throughout the part III to help students apply coding skills to real-world questions that can be explored using big data distributed by the instructor.
A final project will be due at the end of the semester, in which students will use big data in R to analyze a real-world economic issue.

<aside> 💡

All assignment details can be found on the Assignments page in Canvas.

</aside>

In addition to these assignments, students are expected to engage actively in class discussions.

15 counted out of 18 mini-quizzes: 15%
1 Midterm exam: 25%
3 Problem sets: 15%
1 Group presentation: 20%
1 Final project: 25%
Bonus points will be available up to 10% depending on participation in in-class activities.

A 93%-100%

A- 92%-90% B+ 87%-89%

B 83%-86% C+ 77%-79%