Welcome to the Spring 2024 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.
Mondays and Wednesdays 1-2 PM in Welch 5.228G. The best way to get here is to enter into Welch Hall through the glass doors along the Speedway side of the building. You should be in a large atrium with a large video screen on the far wall. Please use the elevator on the rightmost (northern) side of this atrium and head up to the 5th floor. Take a left out of the elevator and then an immediate right down the hallway. Enter through the main office in Welch 5.216, which has big-frosted glass walls. Proceed from there to the hallway with my office.
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; Jupyter; the importance of replicable analyses; version control with Git and Github.
Resources to learn Github and RMarkdown:
- Introduction to RMarkdown and RMarkdown tutorial
- Introduction to GitHub
- Getting starting with GitHub Desktop
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:
Principal component analysis (PCA). T-distributed stochastic neighbor embedding (tSNE).
Reading: rest of chapter 10 of Introduction to Statistical Learning.
- pca_intro.R
- nbc.R, nbc_showdetails.csv, nbc_pilotsurvey.csv
- congress109.R
- ercot_PCA.R, ercot.zip
- tSNE.ipynb
Intro to neural network slides here. Jupyter notebooks here.
Slides on association rules here.
Miscellaneous:
- Gephi, a great piece of software for exploring graphs
- The Gephi quick-start tutorial
Scripts and data:
Treatment effects; multi-armed bandits and Thompson sampling; high-dimensional treatment effects with the lasso.
Slides:
Scripts and data: