Statistics Major Electives in 2025-26
Please note the prerequisites in the tables below are just a summary. Please refer to the Statistics College Catalog for the detailed prerequisites and course descriptions. See Graduate Announcements for the detailed prerequisites and course descriptions of STAT 3XXXX courses.
How Do Undergrads Enroll in Graduate Courses? Several electives below have graduate courses numbers only. Undergrads CANNOT see or enroll in graduate courses ONLINE at my.uchicago. Nonetheless, undergrads may enroll in graduate courses by instructor consent. To enroll in a Graduate course, please contact the instructor of the course for permission to enroll and explain why you are interested in taking the course. The instructor might ask if you have the appropriate prerequisite for the course. If the instructor agrees, you can download and fill out the printable consent form, send the filled form to the instructor by email or in-person for a signature, and then delivered the signed form to the Registrar’s Office by email (register@uchicago.edu) or in-person by the end of Week 3.
| Quarter | List | Course Number | Course Title | Prerequisites |
|---|---|---|---|---|
| Spr 2026 | B | STAT 22200 | Linear Models and Experimental Design | STAT 220 or 234 or 245 |
| Win 2026 | B | STAT 226003 | Analysis of Categorical Data | STAT 220 or 234 or 245 |
| Spr 2026 | B | STAT 22700 = PBHS 3273 | Biostatistical Methods | STAT 224 or 245 |
| Spr 2026 | C | STAT 24310 | Numerical Linear Algebra: An Introduction to Computation | STAT 24300 or equivalent |
| Spr 2026 | A/B | STAT 24620 | Multivariate Statistical Analysis: Applications and Techniques | STAT 245 |
| Aut 2025 | A/B | STAT 24630 | Causal Inference Methods and Case Studies | STAT 23400 + (STAT 251 or 24400) |
| Win 2026 | C | STAT 253001 | Introduction to Probability Models | STAT 244 or 251 |
| Spr 2026 | A/B | STAT 26100 | Time Dependent Data | STAT 245 |
| N/A | A/B | STAT 26300 | Introduction to Statistical Genetics | STAT 245 |
| Win 2026 | A/B | STAT 27400 | Nonparametric Inference | STAT 244 |
| Win 2026 | B | STAT 27410 | Introduction to Bayesian Data Analysis | (STAT 234 or 244) + (STAT 224 or 226 or 245) |
| Spr 2026 | B | STAT 27420 | Introduction to Causality with Machine Learning | STAT 245 or 27725 |
| Aut 2025 Win 2026 |
C | STAT 27725 = CMSC 25400 | Machine Learning | STAT 27700 or 243 or 245 |
| Aut 2025 | C | STAT 27815 | Practical R Programming | STAT 220 or higher |
| Win 2026 | A/B | STAT 27850 | Multiple Testing, Modern Inference, and Replicability | STAT 244 |
| Aut 2025 | B | STAT 27855 | Hypothesis Testing with Empirical Bayes Methodology | STAT 244 or 24410 |
| Win 2026 Spr 2026 |
C | STAT 280004 | Optimization | MATH 205 + STAT 243 |
| Aut 2025 | C | MATH 235001,2 | Markov Chains, Martingales, and Brownian Motion | STAT 251 or 244 |
| Aut 2025 | C | STAT 30900 | Mathematical Computation I: Matrix Computation Course | STAT 243 + Some Statistics |
| Spr 2026 | C | STAT 31015 | Mathematical Computation IIA: Convex Optimization | STAT 309 or STAT 31430 or instructor consent |
| Win 2026 | C | STAT 31020 | Mathematical Computation IIB: Nonlinear Optimization | STAT 309 or STAT 31430 or instructor consent |
| N/A | C | STAT 31150 | Inverse Problems and Data Assimilation | STAT 244 + Linear Algebra + a bit ODE |
| Win 2026 (?) | C | STAT 31511 | Monte Carlo Simulation | Multivar-Calc + LinAlg + basic ODE |
| Aut 2025 | C | STAT 312001 | Introduction to Stochastic Processes I | STAT 251 or 244 |
| Win 2026 | B | STAT 33100 | Sample Surveys | ? |
| ?? | B | STAT 37601 = CMSC 25025 | Machine Learning and Large-Scale Data Analysis | (STAT 27700 or 27725) + STAT 244 |
| Spr 2026 | A/B | STAT 34800 | Modern Methods in Applied Statistics | STAT 245 + 343 |
| Spr 2026 | C | STAT 377105 | Machine Learning | |
| Aut 2025 | C | STAT 377115 = DATA 37711 | Foundations of Machine Learning and AI - Part I | |
| Win 2026 | C | STAT 38100 | Measure-Theoretic Probability I | STAT 304 or Instructor Consent |
| Spr 2026 | C | STAT 38300 | Measure-Theoretic Probability III | STAT 381 |
| Win 2026 | C | STAT 38510 | Brownian Motion and Stochastic Calculus | STAT 383 or MATH 312-313-314 |
| Win 2026 | C | STAT 39000=FINM 34500 | Stochastic Calculus | |
| Aut 2025 | C | TTIC 31020 | Intro to Machine Learning | |
| Spr 2026 | B | TTIC 31180 | Probabilistic Graphical Models | Machine Learning |
| N/A | C | TTIC 31230 | Fundamentals of Deep Learning | |
| Win 2026 | B | PBHS 33300=STAT 36900 | Applied Longitudinal Data Analysis | STAT 224+(226 or 227) |
| Aut 2025 | B | PBHS 33400 | Multilevel Modeling | STAT 224+(226 or 227) |
| Aut 2025 | B | PBHS 33500 = STAT 35800 | Statistical Application | STAT 224 or 226 or 227 |
| Win 2026 | B | PBHS 43010=STAT 35920 | Applied Bayesian Modeling and Inference | STAT 244-245 |
| N/A | B | BUSN 41201 | Big Data | STAT 224 |
| Win 2026 | B | BUSN 41910=STAT 33500 | Time-Series Analysis for Forecasting and Model-Building | BUSN 41901 or Instructor Consent |
1 Warning: Take at most one of MATH 23500, STAT 25300, or STAT 31200
Students may count only one of the 3 courses: MATH 23500, STAT 25300, or STAT 31200, toward the Statistics major.
2 If MATH 23500 is counted in place of STAT 25100, then it cannot also count as an elective.
3 Warning: Do Not take both STAT 22600 & STAT 22700=PBHS 32700
Students may count either STAT 22600 or STAT 22700=PBHS 32700, but not both, toward the Stat major.
4 Warning: For the BA in Statistics, STAT 28000 counts as a List C elective. For the BS in Statistics, STAT 28000 counts as a List C elective only if MATH 21100 is also included in the program. In other words, for the BS, students cannot double-count STAT 28000 toward both the four-elective requirement and the requirement to take at least one of STAT 28000 and MATH 21100.
5 Do Not take both STAT 37710 & STAT 37711=DATA 37711
Students may count either STAT 37710 & STAT 37711=DATA 37711, but not both, toward the Stat major.
Courses That Are NOT Electives for Statistics Major
| Course Number | Course Title |
|---|---|
| STAT 11800 | Introduction to Data Science I |
| STAT 11900 | Introduction to Data Science II |
| STAT 20000 | Elementary Statistics |
| STAT 20010 | Elementary Statistics Through Case Study |
| STAT 22000 | Statistical Methods and Applications |
| STAT 23400 | Statistical Models and Methods |
| STAT 22810 = ENST 27400 = HLTH 20910 | Epidemiology and Population Health |
| STAT 27700 | Mathematical Foundations of Machine Learning |
| STAT 28200 | Dynamical Systems with Applications |
| STAT 29700 | Undergraduate Research |
| STAT 29900 | Bachelor’s Paper |
| STAT 31190 = CAAM 31190 | Fast Algorithms |
| STAT 31250 = CAAM 31250 | Mathematical Introduction to Topological Insulators |
| STAT 31410 = CAAM 31410 | Applied Dynamical Systems |
| STAT 31430 = CAAM 31430 | Applied Linear Algebra |
| STAT 31440 = CAAM 31440 | Applied Analysis |
| STAT 31900 = CHDV 30102 = CHDV 20102 | Introduction to Causal Inference |
| STAT 32400 = BUSN 41901 | Probability and Statistics |
DISCLAIMER: These guidelines do not supersede or replace the University’s offical publications on course prerequisites, course offerings, or the Statistics Major degree program description. See the College Catalog, Course Schedule, and my.UChicago for details.
Maintained by the Coordinator of Statistics Undergraduate Programs, Yibi Huang (yibih@uchicago.edu)
Information valid as of October 4, 2025.