Data science for economists

This is a graduate economics seminar taught by Grant McDermott at the University of Oregon.

Please read the syllabus before you go through any of the lectures. This will detail software requirements and installation, and give you a better sense of the aims and scope of the course. I also have some minor requests (in the "FAQ" section right at the end) if you are interested in adapting the material here for your own course.

How do I download this material and keep up to date with any changes?

Please note that this is a work in progress, with new material being added every week.

If you just want to read the lecture slides or HTML notebooks in your browser, then you should simply scroll down to the Lecture outline and quicklinks section at the bottom of this page. Completed lectures will be hyperlinked as soon as they have been added. Remember to check back in regularly to get any updates.

If you actually want to run the analysis and code on your own system (highly recommended), then you will need to download the material to your local machine. The best way to do this is to clone the repo via Git and then pull regularly to get updates. Please take a look at these slides if you are unfamiliar with Git or are unsure how to do any of that. Once that's done, you will find each lecture contained in a numbered folder (e.g. 01-intro). The lectures themselves are written in R Markdown and then exported to HMTL format. Click on the HTML files if you just want to view the slides or notebooks.

I've spotted a mistake or would like to contribute

Please open a new issue. Better yet, please submit an upstream pull request. I'm very grateful for any contributions, but may be slow to respond while this course is still be developed. Similarly, I am unlikely to help with software troubleshooting or conceptual difficulties for non-enrolled students. Others may feel free to jump in, though.

Are you willing to teach a version of this course at my university?

Possibly. Please contact me if you would like to discuss further.

Lecture outline and quicklinks

1. Introduction: Motivation, software installation, and data visualization [Slides.]
2. Version control with Git(Hub) [Slides.]
3. Learning to love the shell [Slides.]
4. R language basics [Slides.]
5. Data wrangling and tidying with the “Tidyverse” [Slides. Notebook.]
6. Webscraping: (1) Server-side and CSS [Notebook.]
7. Webscraping: (2) Client-side and APIs [Notebook.]
8. Regression analysis in R
9. Spatial analysis in R
10. Functions in R: (1) Introductory concepts
11. Functions in R: (2) Advanced concepts
12. Parallel programming
13. Docker
14. Virtual machines / cloud servers (Google Compute Engine)
15. High performance computing (UO Talapas cluster)
16. Databases: SQL(ite) and BigQuery
17. Spark
18. Machine learning: (1)
19. Machine learning: (2)