Data Science for Economists
Hands-on learning with cutting-edge tools and methodologies, from web scraping to satellite imagery analysis.
Summary
This course provides a snapshot of state-of-the-art research in the field of international economics that makes use of big, often unconventional datasets, and novel methods.
Topics include issues in development studies (e.g. using satellite imagery), international trade and migration (large semi-structured administrative data and cellphone trace data) and international finance (data from social networks).
The course combines a weekly lecture that introduces one or more research projects, their data and methods, as well as an application session in which students are tasked with handling similar datasets and methods.
Coursework includes short assignments along the semester, as well as a final project.
Topics
Schedule
The course takes place on Wednesdays from 10 – 12h and 16 – 18h.
Date | Time | Location | Topic | Teacher |
---|---|---|---|---|
April 9 | 16 – 18 | X-E0-216 | Course outlook and reproducibility | Julian & Irene |
April 16 | 10 – 12 | U2-113 | R, bash, make and git | ? |
16 – 18 | X-E0-216 | |||
April 23 | 10 – 12 | U2-113 | Large structured data | ? |
16 – 18 | X-E0-216 | |||
April 30 | 10 – 12 | U2-113 | Web scraping, APIs and databases | ? |
16 – 18 | X-E0-216 | |||
May 7 | 10 – 12 | U2-113 | Networks | ? |
16 – 18 | X-E0-216 | |||
May 14 | 10 – 12 | U2-113 | ML, OCR and LLMs ? | ? |
16 – 18 | X-E0-216 | |||
May 21 | 10 – 12 | U2-113 | Spatial data | ? |
16 – 18 | X-E0-216 | |||
May 28 | 10 – 12 | U2-113 | Satellite imagery | ? |
16 – 18 | X-E0-216 | |||
June 4 | 10 – 12 | U2-113 | Event and sensor data | ? |
16 – 18 | X-E0-216 | |||
June 11 | 10 – 12 | U2-113 | Text as data | Irene |
16 – 18 | X-E0-216 | |||
June 18 | 10 – 12 | U2-113 | Social media data | Irene |
16 – 18 | X-E0-216 | |||
June 25 | 10 – 12 | U2-113 | ? | ? |
16 – 18 | X-E0-216 |
References and additional resources
The course material, both for the lectures and coding examples, is often inspired by fantastic work from other educators and researchers. Here are some references that sometimes go beyond what we do in class:
Kieran Healy’s Plain Text Guide to Social Science
Hadley Wickham and Garrett Grolemund’s R for Data Science
Grant McDermott’s Data Science for Economists
Jenny Bryan’s Stat 545
and a bit more towards computer science, The Missing Semester of Your CS Education
Questions?
Any general questions? Post them in the dedicated #general Slack Channel in case you think this is a question of general interest. Of course, you can also contact us privately