The Statistics undergraduate curriculum offers an engaging, flexible, and high-quality experience. It emphasizes modern statistical and computational methods, strong communication skills, and extensive hands-on experience with the analysis of data from real, interdisciplinary problems. (We have no “textbook” datasets after the intro level.) With several majors and tracks, and options within each, the curriculum can be tailored to meet your goals and interests.

Data skills and statistical thinking are increasingly sought after by corporations, government, and researchers in many areas. A degree in Statistics at Carnegie Mellon will prepare you to meet that demand. In addition our program well prepares students for Master's or Ph.D. degrees in Statistics or related fields.

Check out the programs below.

Majors

Core Statistics Major

The essential tools and ideas of statistical theory and practice.

Howard Seltman
Joel Greenhouse
Statistics and Machine Learning Major

Methods and algorithms for automated learning and discovery

Ryan Tibshirani
Statistics and Neuroscience Track

Models and analysis of brain systems via neuronal and neuroimaging data.

Valerie Ventura
Mathematical Statistics Track

Fundamental mathematical concepts, models, and tools underlying statistical inference and analysis.

Cosma Shalizi
Economic Statistics Major

Statistical modeling and methodology to the empirical analysis of economic and financial data.

Rebecca Nugent

Paige Houser is the Academic Advisor for the program and the point of contact for most questions. She will guide you to appropriate faculty advisors.

The Statistics Minor

The Minor in Statistics develops skills that complement major study in other disciplines. The program helps the student master the basics of statistical theory and advanced techniques in data analysis. This is a good choice for deepening understanding of statistical ideas and for strengthening research skills.

Requirements

Course Topic/Title Course Number Units Prerequisites Theory Requirements Calculus 21-111 and 112, or 21-120 20 or 10 Multivariate 21-256, 21-259, or 21-268 9–10 21-112 or 21-120 Linear/Matrix Algebra 21-240, 21-241, or 21-242 10 Probability 36-217, 21-325, 15-359, or 36-225 9 21-112, 21-122, 21-123, 21-256, or 21-259 Statistical Inference 36-226 or 36-326 9 C or higher in 36-217, 36-225, 21-325, or 15-359 Data-Analysis Requirements Beginning Data Analysis 36-201, 36/70-207, 36-220, or 36-247 9 Intermediate Data Analysis 36-202, 36/70-208, or 36-309 9 Modern Regression 36-401 9 C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241 Advanced Methods for Data Analysis 36-402 9 C or higher in 36-401

It is departmental policy that students must have at least three statistics courses that do not count for their primary major. If students do not have at least three, they typically take additional advanced electives.

Research Opportunities

There are several mechanisms for undergraduates to participate in research. These include:

• For credit as part of an Independent Study course (36-295, 36-395, 36-495)
• In the summer, with a paid stipend, as part of the Statistics Department Summer Research Program (send mail to summer@stat.cmu.edu)
• As a part time job with funding through the Statistics Department's NSF Research Training Grant