Undergraduate Programs in Statistics

We have developed an engaging, flexible, and high-quality curriculum that can be tailored to your needs and interests.

We emphasize modern methods, strong communication skills, and the analysis of data from real, interdisciplinary problems (with no “textbook” datasets after the intro level).

With a mastery of data becoming ever more important in so many career directions, we can prepare you with the skills and experience to succeed.

For more information...

Find what you need on the undergraduate website

The Master's of Statistical Practice Program

Carnegie Mellon's premier professional training in data science.

This is a one-year, two-semester professional masters degree program that emphasizes statistical practice, methods, data analysis and practical workplace skills.

Applications are accepted on a rolling schedule beginning now through 1 February 2016.

What you'll learn

The Master's of Statistical Practice degree is an intensive two-semester professional master's program that emphasizes statistical practice, methods, data analysis, and practical workplace skills. The MSP is for students who are interested in professional careers in business, industry, government, marketing, or scientific research, or continuing in graduate school. There is no thesis requirement for this program.

One of the hallmarks of the MSP program is our course, 36-726 "Statistical Practice". The focus of this course is a consulting project. These projects come from companies in and around Pittsburgh as well as on campus.

For more information...

Find what you need on the official MSP website

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The Masters of Statistical Practice Program application deadline for 2016 is February 1st, 2016 at 11PM.

The Ph.D. Program in Statistics

The program of study leading to the degree of Doctor of Philosophy in Statistics seeks to strike a balance between theoretical and applied Statistics. The Ph.D. program prepares you for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods.

Recent Ph.D. Dissertations

Following is a list of students who received their Ph.D. degree from the department between 1991 and 2014, and where they are currently employed.


Classification Via Auxiliary Information, Beatriz Estefania Etchegaray; IBM Research Postdoc

A Spectral Series Approach to High-Dimensional Nonparametric Inference, Rafael Izbicki; Assistant Professor, Dept of Statistics, Federal University of São Carlos, Brazil

Level Set Trees for Applied Statistics, Brian Kent; Dato

Frequently Asked Questions

Each year we accept into our Ph.D. program a variety of students with very different backgrounds. Our graduate students come from many disciplines, including mathematics, engineering, the sciences, economics, psychology, and management. Indeed, the diversity of backgrounds of our graduate students is one of the strengths of our department.

Hence, there is no one criterion we use to determine who to accept into our program. Of course, a strong academic background is a necessity. To begin graduate study in Statistics, it is essential that you know linear algebra and advanced calculus. Ideally you should also have had at least a semester each of mathematical probability and mathematical statistics. Beyond that, we are quite flexible.

To help you decide if our department is suitable for you, here are answers to some commonly asked questions.

The Ph.D. Program application deadline for 2017 has passed. The deadline was December 15th, 2016 at 11PM.

For more information...

Find what you need on the Ph.D. website