POL 676: Statistical Analysis of Big Data (Spring 2026)

Course Material
Syllabus
Optimization Example
Bayesian Example 1 (JAGS code)
Bayesian Example 2 (data)
Scraping Example 1
Scraping Example 2
Topics Example 1
Topics Example 2
Supervised Learning Example 1
Supervised Learning Example 2
Nonparametrics Example 1
Nonparametrics Example 2
Ideal Point Estimation Example 1
Ideal Point Estimation Example 2
Ideal Point Estimation Example 3
Ideal Point Estimation Example 4
Ideal Point Estimation Example 5
Ideal Point Estimation Example 6
Neural Networks Example 1
Neural Networks Example 2
Neural Networks Example 3
Intro Slides
MLE Review Slides
Bayesian Slides
Text Analysis Slides
Machine Learning Slides
Nonparametrics Slides
Ideal Point Estimation Slides
Neural Networks Slides
Assignment 1 (data) (jackman's appendix)
Assignment 2 (data)
Assignment 3 (data) (data)
Python Notebook
Course Schedule
Class 1 (Jan. 28) - Intro, Bayesian Statistics
Class 2 (Feb. 4) - Bayesian Statistics
Class 3 (Feb. 11) - Bayesian Statistics
Class 4 (Feb. 18) - Text Analysis
Class 5 (Feb. 25) - Machine Learning
Class 6 (Mar. 4) - Machine Learning
Class 7 (Mar. 11) - Neural Networks
Spring Break
Class 8 (Mar. 25) - Neural Networks
Class 9 (Apr. 1) - Neural Networks
Class 10 (Apr. 8) - Ideal Point Estimation
Class 11 (Apr. 15) - Ideal Point Estimation
Class 12 (Apr. 22) - Nonparametrics
Class 13 (Apr. 29) - Discussion of Papers
Class 14 (May. 6) - Discussion of Papers