markbabbe/sta380

STA 380: Predictive Modeling

STA 380: Predictive Modeling

Welcome to part 2 of STA 380, a course on predictive modeling in the MS program in Business Analytics at UT-Austin. All course materials can be found through this GitHub page. Please see the course syllabus for links and descriptions of the readings mentioned below.

Office hours

On Tuesday-Thursday, August 8-10 and August 15-17, I will hold office hours from 9-10 AM in CBA 6.478.

Exercises

The first set of exercises is available here.

The second set of exercises is available here.

Topics

(0) The data scientist's toolbox

Good data-curation and data-analysis practices; R; Markdown and RMarkdown; the importance of replicable analyses; version control with Git and Github.

Readings:

• a few introductory slides
• Jeff Leek's guide to sharing data
• Introduction to RMarkdown
• Introduction to GitHub

(1) Exploratory analysis

Contingency tables; basic plots (scatterplot, boxplot, histogram); lattice plots; basic measures of association (relative risk, odds ratio, correlation, rank correlation)

Scripts and data:

• gdpgrowth.R and gdpgrowth.csv
• titanic.R and TitanicSurvival

Readings:

• excerpts from my course notes on statistical modeling
• NIST Handbook, Chapter 1.
• R walkthroughs on basic EDA: contingency tables, histograms, and scatterplots/lattice plots.
• Bad graphics
• Good graphics: scan through some of the New York Times' best data visualizations

(2) Foundations of probability

Basic probability, and some fun examples. Joint, marginal, and conditional probability. Law of total probability. Bayes' rule. Independence. These are videos on UT Box.. You will need to sign up for UT Box with your UT e-mail account in order to access these. Please watch these videos before class on Tuesday, 8/8.

Readings:

• basic set notation: unions, intersections, etc.
• excerpts from an in-progress book on probability.

Some optional stuff:

• some more technical notes of probability, for reference
• Bayes and the search for Air France 447.
• YouTube video on Bayes and the USS Scorpion.

(3) Resampling methods

The bootstrap and the permutation test; joint distributions; using the bootstrap to approximate value at risk (VaR).

Scripts:

• gonefishing.R and gonefishing.csv
• greenbuildings.R and greenbuildings.csv
• montecarlo.R
• portfolio.R

Readings:

• ISL Section 5.2 for a basic overview.
• These notes on bootstrapping and the permutation test.
• Section 2 of these notes, on bootstrap resampling. You can ignore the stuff about utility if you want.
• This R walkthrough on using the bootstrap to estimate the variability of a sample mean.
• Another R walkthrough on the permutation test in a simple 2x2 table.
• Any basic explanation of the concept of value at risk (VaR) for a financial portfolio, e.g. here, here, or here.

Optionally, Shalizi (Chapter 6) has a much lengthier treatment of the bootstrap, should you wish to consult it.

(4) Clustering

Basics of clustering; K-means clustering; hierarchical clustering.

Scripts and data:

• protein.R and protein.csv
• cars.R and cars.csv
• we8there.R
• hclust_examples.R

Readings:

• ISL Section 10.1 and 10.3 or Elements Chapter 14.3 (more advanced)
• K means examples: a few stylized examples to build your intuition for how k-means behaves.
• Hierarchical clustering examples: ditto for hierarchical clustering.
• K-means++ original paper or simple explanation on Wikipedia. This is a better recipe for initializing cluster centers in k-means than the more typical random initialization.

(5) Latent features and structure

Principal component analysis (PCA).

Scripts and data:

• pca_2D.R
• pca_intro.R
• congress109.R, congress109.csv, and congress109members.csv
• gasoline.R and gasoline.csv
• FXmonthly.R, FXmonthly.csv, and currency_codes.txt
• cca_intro.R, mmreg.csv, and mouse_nutrition.csv

Readings:

• ISL Section 10.2 for the basics or Elements Chapter 14.5 (more advanced)
• Shalizi Chapters 18 and 19 (more advanced). In particular, Chapter 19 has a lot more advanced material on factor models, beyond what we covered in class.

(6) Networks and Association Rules

Networks and association rule mining.

Scripts and data:

• playlists.R and playlists.csv

Readings:

• Pradeep Ravikumar's notes on association rule mining
• In-depth explanation of the Apriori algorithm

Miscellaneous:

• a little Python utility for scraping Spotify playlists

(7) Text data

Co-occurrence statistics; naive Bayes; TF-IDF; topic models; vector-space models of text (if time allows).

Scripts and data:

• textutils.R
• nyt_stories.R and selections from the New York Times.
• tm_examples.R and selections from the Reuters newswire.
• naive_bayes.R
• simple_mixture.R
• congress109_topics.R

Readings:

• Stanford NLP notes on vector-space models of text, TF-IDF weighting, and so forth.
• Great blog post about word vectors.
• Using the tm package for text mining in R.
• Dave Blei's survey of topic models.
• A pretty long blog post on naive-Bayes classification.