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.

You can find the up-to-date collection of scribe notes here.

The first set of exercises is available here.

The readings listed below are not yet complete, but the topics list is accurate.

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

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:

• 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

The bootstrap and the permutation test; joint distributions; basic moment identities for linear combinations; using the bootstrap to approximate value at risk (VaR).

Scripts:

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

Readings:

• ISL Section 5.2 for a basic overview.
• These notes, pages 99-111. This is an introduction to the bootstrap from the (by now familiar) perspective of linear regression modeling, but it conveys the essential idea.
• 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.

Basics of clustering; K-means clustering; mixture models; 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
• 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.

Principal component analysis (PCA); factor analysis; canonical correlation analysis; multi-dimensional scaling.

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
• Shalizi Chapters 18 and 19 (more advanced). In particular, Chapter 19 has a lot more advanced material on factor analysis, beyond what we covered in class.
• Elements Chapter 14.5 (more advanced)

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.
• (Using the tm package)[http://cran.r-project.org/web/packages/tm/vignettes/tm.pdf] for text mining in R.
• Dave Blei's survey of topic models.
• A pretty long blog post on naive-Bayes classification.

Coverage of these topics will depend on the time available. Possibilities include: anomaly detection; label propagation; learning association rules; graph partitioning; partial least squares.

Scripts and data:

• playlists.R and playlists.csv

Readings:

• Pradeep Ravikumar's notes on association rule mining