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 lecture slides and material being added every week. The easiest way to to stay up to date is to clone this repo to your local machine and pull regularly to get any changes. Please take a look at these slides if you are unfamiliar with Git or are unsure how to do any of that. Each lecture is contained in a numbered folder (e.g. 01-intro). The lectures are written in R Markdown and typically exported to HMTL format. (Click on the HTML files if you just want to view the slides or notebooks.)

Alternatively, I'll be adding hyperlinks to completed lectures in the section below. Any changes that I make to already published slides/notebooks should be reflected automatically.

Lecture outline and quicklinks

1. Introduction: Motivation, software installation, and data visualization
2. Version control with Git(Hub)
3. Learning to love the shell
4. R language basics
5. Data cleaning and wrangling with the “Tidyverse”
6. Webscraping: (1) Server-side and CSS
7. Webscraping: (2) Client-side and APIs
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)