Statistics Major Electives AY 2026-27

Published

July 24, 2026

Please note that the prerequisites listed in the tables below are only summaries. For detailed prerequisites and course descriptions, please consult the Statistics College Catalog. For STAT 3XXXX courses, please see the Graduate Announcements.

How Do Undergraduates Enroll in Graduate Courses?

Several electives listed below are offered only under graduate course numbers (3XXXX). Undergraduate students cannot view or enroll in graduate courses online through my.uchicago. Undergrads cannot pre-register or bid for graduate courses either. Nonetheless, undergraduates may enroll in graduate courses with instructor consent during the Add-Drop-Consent period

To request enrollment in a graduate course, please contact the instructor directly, explain why you are interested in the course, list the MATH & STAT courses and relevant prerequisite courses you have completed, and request permission to enroll.

If the instructor approves your request, please download and fill out the printable consent form. You may then send the filled form to the instructor by email or in person for signature. Once signed, the form must be submitted to the Registrar’s Office either by email at register@uchicago.edu or in person by the end of Week 3.

Approved Electives for the Statistics Major

Quarter List Course Number Course Title Prerequisites
Spr 2027 B STAT 22200 Linear Models and Experimental Design STAT 220 or 234 or 245
Win 2027 B STAT 226003 Analysis of Categorical Data STAT 220 or 234 or 245
Spr 2027 B STAT 22700 = PBHS 3273 Biostatistical Methods STAT 224 or 245
Win 2027 C STAT 24310 Numerical Linear Algebra: An Introduction to Computation STAT 24300 or equivalent
Spr 2027 A/B STAT 24620 Multivariate Statistical Analysis: Applications and Techniques STAT 245
Aut 2026 A/B STAT 24630 Causal Inference Methods and Case Studies STAT 23400 + (STAT 251 or 24400)
Win 2027 C STAT 253001 Introduction to Probability Models STAT 244 or 251
Spr 2027 A/B STAT 26100 Time Dependent Data STAT 245
Spr 2027 A/B STAT 26300 Introduction to Statistical Genetics STAT 245
Win 2027 A/B STAT 27400 Nonparametric Inference STAT 244
Win 2027 B STAT 27410 Introduction to Bayesian Data Analysis (STAT 234 or 244) + (STAT 224 or 226 or 245)
Spr 2027 B STAT 27420 Introduction to Causality with Machine Learning STAT 245 or 27725
Win 2027 C STAT 27725 = CMSC 25400 Machine Learning STAT 27700 or 243 or 245
Sum 2026
Aut 2026
C STAT 27815 Practical R Programming STAT 220 or higher
N/A A/B STAT 27850 Multiple Testing, Modern Inference, and Replicability STAT 244
Aut 2026 B STAT 27855 Hypothesis Testing with Empirical Bayes Methodology STAT 244 or 24410
Win 2027
Spr 2027
C STAT 280004 Optimization MATH 205 + STAT 243
Aut 2026
Spr 2027
C MATH 235001,2 Markov Chains, Martingales, and Brownian Motion STAT 251 or 244
Aut 2026 C STAT 30900 Mathematical Computation I: Matrix Computation Course STAT 243 + Some Statistics
Win 2027 C STAT 31015 Mathematical Computation IIA: Convex Optimization STAT 309 or STAT 31430 or instructor consent
Win 2027 C STAT 31020 Mathematical Computation IIB: Nonlinear Optimization STAT 309 or STAT 31430 or instructor consent
Win 2027 C STAT 31511 Monte Carlo Simulation Multivar-Calc + LinAlg + basic ODE
Aut 2026 C STAT 312001 Introduction to Stochastic Processes I STAT 251 or 244
Win 2027 B STAT 33101 Survey Sampling and Design of Experiments ?
Aut 2026 B STAT 33910 = FINM 34600 The Analysis of High Frequency Data STAT 39000
Spr 2027 A/B STAT 34800 Modern Methods in Applied Statistics STAT 245 + 343
N/A B STAT 37601 = CMSC 25025 Machine Learning and Large-Scale Data Analysis (STAT 27700 or 27725) + STAT 244
Spr 2027 C STAT 377105 Machine Learning
Aut 2026 C DATA 37711 = STAT 377115 Foundations of Machine Learning and AI - Part I
Win 2027 C DATA 37712 Foundations of Machine Learning and AI - Part II DATA 37711
Win 2027 C STAT 38100 Measure-Theoretic Probability I STAT 304 or Instructor Consent
Spr 2027 C STAT 38300 Measure-Theoretic Probability III STAT 381
Win 2027 C STAT 38510 Brownian Motion and Stochastic Calculus STAT 383 or MATH 312-313-314
MATH 205 or 209 is NOT enough for STAT 385!
Win 2027 C STAT 39000=FINM 34500 Stochastic Calculus (MATH 205 or 209) and (STAT 251 or MATH 235)
Win 2027 C TTIC 31020 Intro to Machine Learning
N/A B TTIC 31180 Probabilistic Graphical Models Machine Learning
Win 2027 C TTIC 31190 Natural Language Processing
Spr 2027 C TTIC 31230 Fundamentals of Deep Learning
? B PBHS 33300=STAT 36900 Applied Longitudinal Data Analysis STAT 224+(226 or 227)
? B PBHS 33400 Multilevel Modeling STAT 224+(226 or 227)
Aut 2026 B PBHS 33500 = STAT 35800 Statistical Application STAT 224 or 226 or 227
? B PBHS 43010=STAT 35920 Applied Bayesian Modeling and Inference STAT 244-245
? B BUSN 41201 Big Data STAT 224
? 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 31050 = CAAM 31050 Applied Approximation Theory
STAT 31190 = CAAM 31190 Fast Algorithms
STAT 31210 = CAAM 31210 Applied Functional Analysis
STAT 31220 = CAAM 31220 Partial Differential Equations
STAT 31250 = CAAM 31250 Mathematical Introduction to Topological Insulators
STAT 31310 = CAAM 31310 Foundations Of Computational Dynamics
STAT 31410 = CAAM 31410 Applied Dynamical Systems
STAT 31430 = CAAM 31430 Applied Linear Algebra
STAT 31440 = CAAM 31440 Applied Analysis
STAT 31450 = CAAM 31450 Applied Partial Differential Equations
STAT 31470 = CAAM 21470/31470 Applied Complex Analysis
STAT 31531 = CAAM 31531 Asymptotic Analysis
STAT 31900 = CHDV 30102 = CHDV 20102 Introduction to Causal Inference
STAT 32400 = BUSN 41901 Probability and Statistics
TTIC 31150 = CMSC 31150 Mathematical Toolkit

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 July 24, 2026.