Welcome to the Spring 2022 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.
For introductory material on data wrangling, we'll rely on Lesson 6 of DSGI. Please read and practice this material thoroughly; we'll practice more class, working through a series of examples.
If you'd like even more review and practice with R, then I'd suggest working your way through Chapters 1-4 of Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, by Ismay and Kim. This is roughly at the same level as our main reference.
A more advanced and much more comprehensive guide can be found in R for Data Science, by Wickham and Grolemund.
For material in class, please download the following data sets and example R script:
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
- a little Python utility for scraping Spotify playlists
A bit on treatment-effect estimation.
In class:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent