This course teaches students to apply statistical methods to learn from data. Topics include one and twosample inference; an introduction to Bayesian inference and associated probability theory; linear and logistic regression models; the bootstrap; and crossvalidation. Students use an integrated statistical computing environment to explore and analyze data, develop models, make inferences, and communicate results in a reproducible manner through a projectoriented approach to learning.
Instructor: Keith Levin, kdlevin  at  wisc  dot  edu  
TAs:  
Nursultan Azhimuratov, azhimuratov  at  wisc  dot  edu  
Alex Hayes, alex.hayes  at  wisc  dot  edu  
Shane Huang, shuang457  at  wisc  dot  edu  
Joseph Salzer, jsalzer  at  wisc  dot  edu  
Lectures:  
Section 001: TuTh, 11:00AM12:15PM in Bardeen 140  
Section 002: TuTh, 2:30PM3:45PM in Van Vleck B130  
Office Hours:  
Keith Levin: Wednesdays 12pm2pm in Medical Science Center 6170  
Nursultan Azhimuratov: Mondays 1pm3pm in Medical Sciences Center 1274  
Alex Hayes: Wednesdays 10am12pm in Medical Sciences Center 1475  
Shane Huang: Tuesdays and Thursdays 1pm2pm in Medical Sciences Center 1274  
Joseph Salzer: Tuesdays 10am11am in Medical Sciences Center 1217C and Tuesdays 5pm6pm in Medical Sciences Center 1274  
Textbook: We will make reference to a variety of textbooks this semester, all available online:  
Introduction to Data Science by Rafael Irizarry  
R for Data Science ("R4DS") by Hadley Wickham and Garrett Grolemund  
Introduction to Probability and Statistics Using R ("IPSUR") by G. Jay Kerns  
Introduction to Probability for Data Science by Stanley H. Chan  
An Introduction to Statistical Learning, 2nd Edition ("ISLR") by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani  
Syllabus: available here  
Prerequisites: MATH 217, 221, or 275 and STAT 240 
Date  Topics  Readings  Notes 
Week 0 Sep 8 
Course introduction and administrivia 


Week 1 Sep 13,15 
Probability review and random variables 


Week 2 Sep 20,22 
Introduction to Monte Carlo 


Week 3 Sep 27,29 
Hypothesis testing 


Week 4 Oct 4,6 
Hypothesis testing, cont'd 


Week 5 Oct 11,13 
Independence, Conditional Probability and Bayes' Rule 


Week 6 Oct 18,20 
Estimation 


Week 7 Oct 25,27 
Estimation, cont'd 


Week 8 Nov 1,3 
Prediction: simple linear regression 


Week 9 Nov 8,10 
Prediction: multiple linear regression 


Week 10 Nov 15,17 
Prediction: logistic regression 


Week 11 Nov 22 
Oneoff lecture: causal inference 


Week 12 Nov 29, Dec 1 
Crossvalidation and model selection 


Week 13 Dec 6,8 
The bootstrap 


Week 14 Dec 13 
Recap and exam review 