Rebecca Nugent, Department Head

Peter Freeman, Director of Undergraduate Studies

Zach Branson, Assistant Director of the Undergraduate Program

Samantha Nielsen, Associate Director of Academic Programs

Amanda Mitchell, Lead Senior Academic Advisor

Glenn Clune, Academic Program Manager

Sylvie Aubin, Academic Program Manager

Peter Long, Academic Advisor

Email: statadvising@andrew.cmu.edu
Location: Baker Hall 129

www.stat.cmu.edu/

In Statistics and Data Science, we train students to be the people who determine the data to collect, visualize and analyze that data, make decisions based on that data, and communicate that data-driven process to collaborators and stakeholders. Because of this, our students regularly get sought-after jobs in many industries and attend top graduate programs.

Curriculum and Training in Statistics and Data Science

Our faculty have built (and continue to build) an innovative curriculum to ensure students have state-of-the-art training and can adapt in an ever-changing, data-driven world. Our curriculum focuses on four areas that are at the foundation of Statistics & Data Science:

• Critical Thinking: Students formulate questions that can be answered with data, and pinpoint how to collect, visualize, and analyze data to answer those questions. Furthermore, students assess when visualization, analysis, and other tools (e.g., models, algorithms, computing, AI) are appropriate, reliable, and trustworthy.

• Methods and Applications: Students implement state-of-the-art methods for processing, visualizing, and analyzing data with computing tools to do these tasks quickly-but-reliably. Students work with real-world scenarios, such that they learn how method choices are informed by the data and the context in which they are used.

• Theory: Students gain the mathematical training necessary to characterize the effectiveness of data analysis methods in different settings (via probability, calculus, and statistical models), including new methods they develop or encounter in the world.

• Communications: Students communicate, argue, and defend complex analyses to collaborators and stakeholders (in writing, presentations, and other modalities), such that data science work informs real-world decisions. This includes open-ended projects via experiential learning opportunities.

Our faculty have nurtured collaborations in many areas (e.g., astronomy, climate science, epidemiology, genetics, medicine, public policy, and sports analytics, to name a few), and the entire faculty teach courses at all levels, giving students different perspectives from world-renowned researchers and educators. Beyond courses, students gain expertise from research and experiential learning opportunities (e.g., our capstone courses, where students build data science products for non-statistical clients) and participate in our friendly, energetic community via student-led clubs and events.

On the remainder of this site, we outline the requirements for our degrees. This includes the core major in Statistics and Data Science, which can be augmented with several disciplines (economics, machine learning, mathematics, or neuroscience). Importantly, the core major involves a self-defined concentration of the student’s choosing (e.g., computer science, business, computational humanities, physics, psychology, public policy), such that students can explore how statistics and data science intersect with other disciplines. We also outline requirements for our minor in Statistics and Data Science.

Note for Current Students: Use this information as a general guide, and then schedule a meeting with a Statistics and Data Science Undergraduate Advisor (statadvising@stat.cmu.edu) to discuss these requirements, so that we can build a program that is tailored to your interests and strengths.

Program Options

Navigating our Degrees

All Statistics and Data Science degrees require students to complete the following training:

• Mathematical Foundations: Calculus and linear algebra, such that students can describe and analyze statistical models and methods.
• Data Analysis: Beginning, intermediate, and advanced data analysis topics, such that students know foundational and state-of-the-art methods for addressing data-specific questions.
• Probability and Statistical Theory: Fundamental tools to estimate unknown quantities (e.g., averages, variances), quantify uncertainty (e.g., confidence intervals), and conduct inference (e.g., hypothesis tests), which are needed for most data analyses.
• Statistical Computing: Computational skills necessary to process, visualize, and analyze data efficiently, as well as implement and develop complex algorithmic methods.
• Modern Regression and Advanced Methodology: Advanced statistical analysis techniques (especially regression and related methods), so that students can make accurate predictions and justified inferential conclusions about real data problems.

In addition to the above core training, each of our five degrees allow students to pursue interests in a different area, examined below.

Statistics and Data Science

Students who pursue the B.S. in Statistics and Data Science complete a “special topics” course to see how advanced techniques are applied to specific contexts (e.g., epidemiology, sports analytics, finance, text analysis - all topics change on a rotating basis). Finally, a unique aspect of this degree is that students form a self-defined “concentration,” which consists of four related courses outside of Statistics and Data Science but where statistics and data science are still relevant. As a result, students get to explore how statistical thinking applies to real-world contexts that are aligned with their interests.

Department Policies

Transfer Credit

Undergraduate students who wish to transfer in credit for courses taken at another institution should familiarize themselves with both the university policies and their home college’s transfer credit policies. Statistics and Data Science students can find the Dietrich college policies here. 

We encourage students considering taking courses outside of CMU to contact their academic advisor to ensure proper planning and guidance. Students must complete this form to request a course to transfer in as any undergraduate course in the Department of Statistics and Data Science (36-000 - 36-599). 

Statistics and Data Science students who wish to transfer coursework from another institution for core Statistics and Data Science credit must adhere to the following guidelines:

• At most four (4) core courses may be transferred in and used to fulfill degree requirements. This applies to primary and additional major students. For the minor, this is reduced to two (2) courses. 

  • Advanced placement or equivalent and courses taken prior to matriculation to Carnegie Mellon University do not factor into the 4 course maximum.

  • Exceptions may be sought through consultation with an academic advisor and the undergraduate program director(s).

• The following courses must be taken in residence at Carnegie Mellon University and are not eligible for transfer:

  • 36-401 Modern Regression

  • 36-402 Advanced Methods for Data Analysis

  • 36-490 Undergraduate Research

  • 36-493 Sports Analytics Capstone

  • 36-497 Corporate Capstone Project

Substitutions and Waivers

Statistics and Data Science department majors and minors seeking substitutions or waivers must consult their academic advisor and complete the appropriate form, provided by the advisor, for any waiver to be considered. The department does not offer course waiver assessments and will not waive prerequisites for prior knowledge unless approved by an outside department. 

Additionally, the department does not provide approval or permission for substitutions or waivers for majors outside of the Department of Statistics and Data Science. If a student wants to replace a Statistics & Data Science course that is a requirement in their primary major, and the substitute course has not been approved as an equivalent through the required form noted in the “Transfer Credit” section above, students must consult their home department primary advisor. 

Dietrich Honors Thesis - Department Requirements

These guidelines apply to students who are completing an honors thesis that has been approved through the Statistics and Data Science department (i.e. our department signs off on the thesis paperwork and the work is completed with a department faculty member). If you are a Statistics and Data Science student pursuing a Dietrich senior honors thesis through another department (i.e. a different department is signing off on it) then these guidelines do not apply.

Eligibility is determined by Dietrich College. Students who are eligible will be notified prior to their senior year. Dietrich College Requirements can be found here. In order to be approved for a thesis with the Statistics and Data Science department the project needs to have a significant statistical component. 

The Dietrich College senior honors thesis is a year-long project. As such, after the fall semester of a student’s senior year a progress report will be due to the Undergraduate Program Director for review by the last day of classes for the fall semester. The progress report should build substantially on the proposal, and lay out what work has been completed as well as an action plan for the spring semester. The progress report should be a minimum of five pages in length.

In alignment with a typical advanced data analysis (ADA) project in the field of Statistics the minimum required length of the final thesis must be fifteen (15) written pages, no more than 18 single-spaced pages, 12-point font. This does not include figures. Further requirements are outlined on the department website.

All theses are due to the Undergraduate Program Director and Department Head at the end of the twelfth (12th) week of classes in spring semester (typically the first week of April).

Research and Graduate School

Research as an Undergraduate

The Statistics and Data Science program encourages students to gain research experience. Opportunities within the department include Summer Undergraduate Research Apprenticeships (SURA), run in association with the university's Office of Undergraduate Research and Scholar Development, and the departmental capstone courses 36-490 Undergraduate Research, 36-493 Sports Analytics Capstone, or 36-497 Corporate Capstone Project (note that these courses require an application). Additionally, students can pursue independent study. For those students who maintain a quality point average of 3.25 overall or above, there is also the Dietrich College Senior Honors Program.

The faculty in the Statistics and Data Science department largely work within the domains of statistical theory and methodological development, areas that require advanced mathematical training. Thus we encourage students to search broadly for research opportunities: faculty, post-doctoral researchers, and graduate students in many departments throughout the university have data to analyze and would welcome the help of undergraduate Statistics and Data Science students.

Recommendations for Prospective Ph.D. Students

Students interested in pursuing a Ph.D. in Statistics or related programs after completing their undergraduate degree are strongly recommended to pursue the B.S. in Statistics and Data Science (Mathematical Sciences Track) or to take additional Mathematics courses. Although 21-240 Matrix Algebra with Applications is recommended for Statistics and Data Science majors, students interested in PhD programs should consider taking 21-241 Matrices and Linear Transformations or 21-242 Matrix Theory instead. Additional courses to consider are 21-228 Discrete Mathematics, 21-341 Linear Algebra, 21-355 Principles of Real Analysis I, and 21-356 Principles of Real Analysis II. We also recommend that students interested in pursuing a Ph.D. gain some research experience during their undergraduate degree, as discussed further in the Research section above. Internships that involve meaningful real data analysis are also looked upon favorably in PhD programs.

Course Descriptions

About Course Numbers:

Each Carnegie Mellon course number begins with a two-digit prefix that designates the department offering the course (i.e., 76-xxx courses are offered by the Department of English). Although each department maintains its own course numbering practices, typically, the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. Depending on the department, xx-6xx courses may be either undergraduate senior-level or graduate-level, and xx-7xx courses and higher are graduate-level. Consult the Schedule of Classes each semester for course offerings and for any necessary pre-requisites or co-requisites.

---

Faculty

SIVARAMAN BALAKRISHNAN, Professor – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2015–

ELI BEN-MICHAEL, Assistant Professor, Joint with Heinz College – Ph.D., University of California; Carnegie Mellon, 2022–

ZACHARY BRANSON, Associate Teaching Professor; Assistant Director for the Undergraduate Program – Ph.D., Harvard University; Carnegie Mellon, 2019–

DAVID CHOI, Associate Professor of Statistics and Information Systems – Ph.D., Stanford University; Carnegie Mellon, 2004–

PETER E. FREEMAN, Associate Teaching Professor; Director of the Undergraduate Program – Ph.D., University of Chicago; Carnegie Mellon, 2004–

CHRISTOPHER R. GENOVESE, Professor – Ph.D., University of California; Carnegie Mellon, 1994–

JOEL B. GREENHOUSE, Professor – Ph.D., University of Michigan; Carnegie Mellon, 1982–

AMELIA HAVILAND, E.J. Barone Professor of Health Systems Management – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2003–

JIASHUN JIN, Professor – Ph.D., Stanford University; Carnegie Mellon, 2003–

ROBERT E. KASS, Maurice Falk Professor of Statistics & Computational Neuroscience – Ph.D., University of Chicago; Carnegie Mellon, 1981–

EDWARD KENNEDY, Associate Professor – Ph.D., University of Pennsylvania; Carnegie Mellon, 2016–

ARUN KUCHIBHOTLA, Associate Professor – Ph.D., University of Pennsylvania; Carnegie Mellon, 2020–

MIKAEL KUUSELA, Associate Professor – Ph.D., Ecole Polytechnique Federale de Lausanne; Carnegie Mellon, 2018–

ANN LEE, Professor, Co-Director of PhD Program – Ph.D., Brown University; Carnegie Mellon, 2005–

JING LEI, Professor – Ph.D., University of California; Carnegie Mellon, 2011–

GONZALO E. MENA, Assistant Professor – Ph.D., Columbia University; Carnegie Mellon, 2023–

DANIEL NAGIN, Teresa and H. John Heinz III Professor of Public Policy – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1976–

NYNKE NIEZINK, Associate Professor – Ph.D., University of Groningen; Carnegie Mellon, 2017–

REBECCA NUGENT, Department Head Stephen E. and Joyce Fienberg Professor of Statistics & Data Science – Ph.D., University of Washington; Carnegie Mellon, 2006–

TAEYONG PARK, Assistant Teaching Professor, CMU-Qatar – Ph.D., Washington University in St. Louis; Carnegie Mellon, 2018–

ANKIT PENSIA, Assistant Professor – Ph.D., University of Wisconsin, Madison; Carnegie Mellon, 2023–

AADITYA RAMDAS, Assistant Professor – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2018–

ALEX REINHART, Associate Teaching Professor – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2018–

KATHRYN ROEDER, UPMC Professor of Statistics and Life Sciences – Ph.D., Pennsylvania State University; Carnegie Mellon, 1994–

CHAD M. SCHAFER, Professor – Ph.D., University of California, Berkeley; Carnegie Mellon, 2004–

TEDDY SEIDENFELD, Herbert A. Simon Professor of Philosophy and Statistics – Ph.D., Columbia University; Carnegie Mellon, 1985–

COSMA SHALIZI, Associate Professor – Ph.D., University of Wisconsin, Madison; Carnegie Mellon, 2005–

YANDI SHEN, Assistant Professor – Ph.D., University of Washington;

WEIJING TANG, Assistant Professor – Ph.D., University of Michigan; Carnegie Mellon, 2023–

WILL TOWNES, Assistant Professor – Ph.D., Harvard University; Carnegie Mellon, 2022–

VALERIE VENTURA, Professor, Co-Director of PhD Program – Ph.D., University of Oxford; Carnegie Mellon, 1997–

ISABELLA VERDINELLI, Professor in Residence – Ph.D., Carnegie Mellon University; Carnegie Mellon, 1991–

LARRY WASSERMAN, UPMC University Professor of Statistics – Ph.D., University of Toronto; Carnegie Mellon, 1988–

RON YURKO, Assistant Teaching Professor – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2022–

Emeriti Faculty

GEORGE T. DUNCAN, Professor of Statistics and Public Policy – Ph.D., University of Minnesota; Carnegie Mellon, 1974–

WILLIAM F. EDDY, John C. Warner Professor of Statistics – Ph.D., Yale Universit; Carnegie Mellon, 1976–

BRIAN JUNKER, Professor – Ph.D., University of Illinois; Carnegie Mellon, 1990–

JOSEPH B. KADANE, Leonard J. Savage Professor of Statistics and Social Sciences – Ph.D., Stanford University; Carnegie Mellon, 1969–

JOHN P. LEHOCZKY, Thomas Lord Professor of Statistics – Ph.D., Stanford University; Carnegie Mellon, 1969–

MARK J. SCHERVISH, Professor – Ph.D., University of Illinois; Carnegie Mellon, 1979–

DALENE STANGL, Teaching Professor – Ph.D, Carnegie Mellon University; Carnegie Mellon, 2017–

Special Faculty

F. SPENCER KOERNER, Lecturer – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2022–

JAMIE MCGOVERN, Director: Master’s in Applied Data Science Program – B.A., Rice University; Carnegie Mellon, 2020–

GORDON WEINBERG, Senior Lecturer – M.A., University of Pittsburgh; Carnegie Mellon, 2004–

Affiliated Faculty

ANTHONY BROCKWELL – Ph.D., Melbourne University; Carnegie Mellon, 1999–

PHILIPP BURCKHARDT – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2022–

BERNIE DEVLIN – Ph.D., Pennsylvania State University; Carnegie Mellon, 1994–

SAM VENTURA – Ph.D., Carnegie Mellon University; Carnegie Mellon, 2015–