Students who enrolled before or in 2023
Major requirements: Students enrolled before or in 2023 must adhere to the major requirements linked here.
Electives: The list of electives for students who enrolled before or in 2023 can be found here.
In Spring 2024, as a valid elective we also consider AMATH 490 (Mathematical Theory of Information Entropy and Data).
All other sections in this page refer to students who will enroll in 2024 and after.
Students who will enroll in 2024 and after
Starting in Autumn 2024, the undergraduate program is organized around three tracks:

Mathematical statistics: this track emphasizes the mathematical foundations of statistical methods.

Data Science: this track emphasizes the link between statistics and computer science

Applied Statistics: this track allows students to explore the use of statistical methods in specific scientific domains.
A minimum GPA of 2.0 and cumulative GPA of 2.5 is required in all courses used to satisfy major requirements.
Core requirements (all tracks): 37 credits

Mathematics: MATH 208 and MATH 224. MATH 208 is waved if MATH 136 was taken.

Computing: STAT 302 and either CSE 163 (strongly recommended) or CSE 123.

Statistical theory: STAT 341 and STAT 342.

Statistical and machine learning methodology: STAT 423, STAT 424, and STAT 435.

Ethics: Either STAT 303 (strongly recommended) or SOC 225 or INFO 350.

Capstone project: STAT 496. Students can petition to have 2 credits of STAT 497 (Undergraduate Statistics Internship) or ENGR 321 (Undergraduate Engineering Internship) substitute for STAT 496.
Breadth requirements (track specific): 1820 credits
Mathematical Statistics Track

One of the following threecourse sequences: (a) MATH 300, MATH 327 and MATH 424, (b) MATH/STAT 395, MATH/STAT 491, and either MATH/STAT 492 or MATH/STAT 493.

Three electives, each at least 3 credits, each from either the general electives list or the computing electives list.
Applied Statistics Track

Three interdisciplinary courses that form a coherent program (see "Interdisciplinary electives" below).

Three electives, each at least 3 credits each, chosen from either the general electives list or the computing electives list.
Data Science Track

Data visualization: One of STAT 451 (highly recommended), CSE 412, CSE 442, HCDE 411, INFO 474.

Databases: One of CSE 414, CSE 444, or INFO 330.

One elective from the computing list.

Three electives, each at least 3 credits each, chosen from either the general electives list or the computing electives list.
At the end of your major, this is how your academic curriculum will look:
List of electives
General electives

CSE 373 Data Structures and Algorithms

CSE 332 Data Structure and Parallelism

MATH 300 Introduction to Mathematical Reasoning

MATH 318 Advanced Linear Algebra: tools and methods

MATH/STAT 395, Probability II

MATH/STAT 396, Finite Markov Chains and MonteCarlo Methods

MATH 407 Linear Optimization

MATH 408 NonLinear Optimization

MATH 409 Discrete Optimizatio

MATH/STAT 491 Introduction to Stochastic Processes

MATH/STAT 492 Introduction to Stochastic Processes II

MATH/STAT 493 Stochastic Calculus for Option Pricing

STAT 403 Introduction to Resampling Inference

STAT 425 Introduction to Nonparametric Statistics

STAT 427 Introduction to Analysis of Categorical Data

STAT 428 Multivariate Analysis for the Social Sciences

STAT 441 Multivariate Statistical Methods

STAT 498 Special Topics

STAT 529: Sample Survey Techniques

STAT 534 Statistical Computing
Computing electives

CSE 373 Data Structures and Algorithms

CSE 332 Data Structure and Parallelism

MATH 318 Advanced Linear Algebra: tools and methods

MATH 407 Linear Optimization

MATH 408 NonLinear Optimization

MATH 409 Discrete Optimization

STAT 534 Statistical Computing
Interdisciplinary electives
Three courses, 300level or higher, offered by another department that form a coherent sequence. Subject to departmental approval.
Example of what your coursework will look like
The following flowcharts will help you understand the STAT 300 level courses and their prerequisites. For a full list of courses please see this page.
STAT 311 and STAT 394 are among the prerequisites for admission and therefore they are usually taken early in the sophomore year (Plan A), so that students can apply to the Stat major in Spring of their sophomore year. Once students enroll in the major, they should start working on the core major requirements by taking STAT 341 and STAT 342, which represent the statistical theory requirements, serve as prerequisites for other core courses and give the foundations to understand Machine Learning classes (such as STAT 435) and other breadth requirements classes. STAT 302 and STAT 303 provide, respectively, the foundations of statistical computing and an understanding of the ethical implications of statistical decisionmaking algorithms. We expect students to take them either during their junior or their senior year. The senior year should be mainly dedicated to the capstone project and to electives; the specific courses will largely depend on the selected track and may require prerequisites.
Students who have AP calculus and CS credits from highschool may be eligible to apply for admission to the Statistics major in their freshmen year (see Plan B).