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.

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)