Welcome to the Spring 2023 edition of ECO 395M, a course on data mining and statistical learning for students in the Master's program in Economics at UT-Austin. All course materials can be found through this GitHub page. Please see the course syllabus for details about:
- expectations
- assignments and grading
- readings
- other important administrative information
The exercises will be posted here as they are assigned throughout the semester.
Tuesday, 1-2 PM, via Zoom (link on Canvas).
Wednesdays in person, 2:30-3:30 PM, CBA 6.478.
I assume that you start the semester with a basic understanding of R and data visualization, at the level of Lessons 1-5 of Data Science in R: A Gentle Introduction. This material was covered in ECO 394D, and although we'll review some of these skills in the course of learning new stuff, it's expected that you're familiar with these lessons from day 1.
Topics: Good data-curation and data-analysis practices; R; Markdown and RMarkdown; the importance of replicable analyses; version control with Git and Github. Visualization and data workflow.
Resources to learn Github and RMarkdown:
- Introduction to RMarkdown and RMarkdown tutorial
- Introduction to GitHub
- Getting starting with GitHub Desktop
Jeff Leek's guide to sharing data is a great resource.
Reading: Chapters 1-2 of "Introduction to Statistical Learning."
In class:
Reading: Chapter 3 of "Introduction to Statistical Learning."
In class:
Reading: Chapter 4 of "Introduction to Statistical Learning."
In class:
- spamtoy.R
- spamfit.csv and spamtest.csv
- glass.R
- glass_mlr.R
- congress109_bayes.R
- congress109.csv
- congress109members.csv
- glass_LDA.R
Reading: chapter 6 of Introduction to Statistical Learning.
In-class:
- saratoga_step.R
- semiconductor.R and semiconductor.csv
- hockey.R and all the files in data/hockey/
- gasoline.R and gasoline.csv
Reading: Chapter 8 of Introduction to Statistical Learning.
The pdp package for partial dependence plots from nonparametric regression models.
Slides here.
Reading: chapter 10.3 of Introduction to Statistical Learning.
In class:
Reading: rest of chapter 10 of Introduction to Statistical Learning.
Slides on association rules here.
Miscellaneous:
- Gephi, a great piece of software for exploring graphs
- The Gephi quick-start tutorial
Treatment effects; multi-armed bandits and Thompson sampling; high-dimensional confounders with the lasso.
Slides:
Scripts and data:
In class: