DSIER [/dɪˈzaɪər/]
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 a research project, its 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.
The course takes place on Wednesdays from 10 – 12h and 16 – 18h, sometimes in person, sometimes online.
Date | Time | Topic | Teacher | Location |
---|---|---|---|---|
April 20 | 10 – 12 | Course outlook | Irene & Julian | U2-228 |
16 – 18 | Good research practice | Julian | U5-133 | |
April 27 | 10 – 12 | The shell | Julian | U2-228 |
16 – 18 | R | Julian | U5-133 | |
May 4 | 10 – 12 | Conventional large data | Irene | U2-228 |
16 – 18 | Julian | jhi.nz/dsier | ||
May 11 | 10 – 12 | Web scraping and APIs | Irene | U2-228 |
16 – 18 | Julian | jhi.nz/dsier | ||
May 18 | 10 – 12 | Networks | Irene | jhi.nz/dsier |
16 – 18 | Julian | jhi.nz/dsier | ||
May 25 | 10 – 12 | Satellite imagery | Irene | U2-228 |
16 – 18 | Julian | jhi.nz/dsier | ||
June 1 | 10 – 12 | Spatial data | Irene | jhi.nz/dsier |
16 – 18 | Julian | jhi.nz/dsier | ||
June 8 | 10 – 12 | Text as data | Irene | U2-228 |
16 – 18 | Julian | jhi.nz/dsier | ||
June 15 | 10 – 12 | Event and sensor data | Irene | jhi.nz/dsier |
16 – 18 | Julian | jhi.nz/dsier | ||
June 22 | 10 – 12 | Social media data | Irene | U2-228 |
16 – 18 | Julian | jhi.nz/dsier | ||
June 29 | 10 – 12 | to be determined | Irene | jhi.nz/dsier |
16 – 18 | Julian | jhi.nz/dsier | ||
July 6 | 10 – 12 | |||
16 – 18 | Presentation of projects | Irene & Julian | U5-133 | |
July 13 | 10 – 12 | |||
16 – 18 | Presentation of projects | Irene & Julian | U5-133 |
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
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
If you see mistakes or want to suggest changes, please create an issue on the source repository.