Carnegie Mellon University
Dietrich College of Humanities and Social Sciences  ›  Statistics & Data Science  ›  Academics  ›  Undergraduate Program  ›  Majors/Minor

Statistics & Data Science Majors/Minor

Statistics & Data Science at Carnegie Mellon equips students to make sense of complex data, build reliable models, and communicate insights that matter.

To do that, our curriculum brings together three connected threads: Statistical Theory, Data Analysis, and Computing.

  • Statistical Theory uses probability to build and study mathematical models, giving us tools for making sound predictions and decisions under uncertainty.
  • Data Analysis focuses on extracting accurate insights from complex data, and checking whether modeling assumptions hold.
  • Computing provides the algorithms, programming skills, and computational frameworks needed to work with large datasets, and turn statistical ideas into practical tools.

These threads reinforce one another. Our curriculum follows all three, helping students develop the theoretical foundation, computational tools, and applied problem‑solving skills needed to work effectively with data across industry, policy, science and beyond.

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Explore our programs to find the right fit for your academic and career goals.

BS in Statistics and Machine Learning BS in Statistics & Data Science BS in Statistics & Data Science (Mathematical Sciences Track) BS in Statistics & Data Science (Neuroscience Track) BS in Economics and Statistics Statistics & Data Science Minor

BS in Statistics and Machine Learning

StatML is designed for students who want to work at the intersection of statistical theory and modern machine learning. It emphasizes rigorous mathematical foundations, algorithmic thinking, and hands-on modeling.

Students in StatML learn to:

  • Build and analyze predictive models using statistical and machine‑learning methods.
  • Understand the mathematical principles behind algorithms and inference.
  • Work with real‑world data using computational tools and reproducible workflows.
  • Evaluate model performance and communicate results clearly.

This major is well‑suited for students interested in data‑driven research, AI‑related fields, or graduate study in statistics, machine learning, or computer science.

  1. Perform a complete and effective analysis of a real data set, choosing appropriate procedures at each step.
  2. Critically assess the strengths and weaknesses of a data analysis and evaluate the validity of the results.
  3. Represent data, code data-driven methods, and competently use a sophisticated statistical computing framework.
  4. Use statistical theory to explain how a statistical methodology works, to identify the assumptions and limitations of a methodology, and to derive a reasonable procedure that solves a statistical problem.
  5. Effectively and clearly communicate statistical methodology and results to statisticians, data scientists, and non-specialists – orally and in writing, and in working cooperatively in multidisciplinary teams.
  6. Describe and illustrate how statistical methods are used to answer questions in disciplines outside of statistics.
  7. Develop well-structured programs to correctly implement important statistical machine learning methods and algorithms.

Details about the StatML curriculum, including specific courses required and sample four-year plans, can be found in the Undergraduate Academic Catalog.

BS in Statistics & Data Science

The StatDS major prepares students to apply statistical reasoning and data‑analytic methods across a wide range of domains. It balances theory, computation, and application, giving students flexibility to explore areas such as public policy, business, health, social science, and technology.

Students in StatDS learn to:

  • Design studies and collect, clean, and structure data.
  • Apply statistical methods to answer substantive questions.
  • Use modern computing tools for data analysis and visualization.
  • Interpret results in context and communicate findings to diverse audiences.

This major is ideal for students who want strong quantitative training with the freedom to connect data science to another field of interest.

  1. Perform a complete and effective analysis of a real data set, choosing appropriate procedures at each step.
  2. Critically assess the strengths and weaknesses of a data analysis and evaluate the validity of the results.
  3. Represent data, code data-driven methods, and competently use a sophisticated statistical computing framework.
  4. Use statistical theory to explain how a statistical methodology works, to identify the assumptions and limitations of a methodology, and to derive a reasonable procedure that solves a statistical problem.
  5. Effectively and clearly communicate statistical methodology and results to statisticians, data scientists, and non-specialists -- orally and in writing, and in working cooperatively in multidisciplinary teams
  6. Describe and illustrate how statistical methods are used to answer questions in disciplines outside of statistics.

Details about the StatDS curriculum, including specific courses required and sample four-year plans, can be found in the Undergraduate Academic Catalog.

BS in Statistics & Data Science (Mathematical Sciences Track)

This track is designed for students who want a deeper mathematical foundation for statistical and data‑analytic work. It strengthens theoretical understanding while maintaining the applied focus of the SDS major.

Students in the Mathematical Sciences track:

  • Build stronger skills in advanced calculus, linear algebra, and probability.
  • Develop a deeper understanding of the mathematical structures behind statistical methods.
  • Prepare for mathematically intensive fields such as quantitative research, actuarial science, or graduate study in statistics or applied math.
  1. Perform a complete and effective analysis of a real data set, choosing appropriate procedures at each step.
  2. Critically assess the strengths and weaknesses of a data analysis and evaluate the validity of the results.
  3. Represent data, code data-driven methods, and competently use a sophisticated statistical computing framework.
  4. Use statistical theory to explain how a statistical methodology works, to identify the assumptions and limitations of a methodology, and to derive a reasonable procedure that solves a statistical problem.
  5. Effectively and clearly communicate statistical methodology and results to statisticians, data scientists, and non-specialists -- orally and in writing, and in working cooperatively in multidisciplinary teams
  6. Describe and illustrate how statistical methods are used to answer questions in disciplines outside of statistics.

Details about the StatDS - Mathematical Sciences Track curriculum, including specific courses required and sample four-year plans, can be found in the Undergraduate Academic Catalog.

BS in Statistics & Data Science (Neuroscience Track)

This track is ideal for students interested in applying data science to the study of the brain and behavior. It integrates core SDS training with coursework in biological sciences and psychology.

Students in the Neuroscience track:

  • Learn how data is collected, analyzed, and interpreted in neuroscience research.
  • Work with high‑dimensional biological and behavioral data.
  • Build quantitative skills relevant to cognitive science, neural data analysis, and health‑related research.
  1. Perform a complete and effective analysis of a real data set, choosing appropriate procedures at each step.
  2. Critically assess the strengths and weaknesses of a data analysis and evaluate the validity of the results.
  3. Represent data, code data-driven methods, and competently use a sophisticated statistical computing framework.
  4. Use statistical theory to explain how a statistical methodology works, to identify the assumptions and limitations of a methodology, and to derive a reasonable procedure that solves a statistical problem.
  5. Effectively and clearly communicate statistical methodology and results to statisticians, data scientists, and non-specialists -- orally and in writing, and in working cooperatively in multidisciplinary teams
  6. Describe and illustrate how statistical methods are used to answer questions in disciplines outside of statistics.

Details about the StatDS - Neuroscience Track curriculum, including specific courses required and sample four-year plans, can be found in the Undergraduate Academic Catalog.

BS in Economics and Statistics

The joint major in Economics & Statistics integrates statistical modeling with economic theory and empirical analysis. Students develop the ability to study markets, policy, and human behavior using rigorous quantitative tools.

Students in EcoStat learn to:

  • Apply statistical and econometric methods to economic questions.
  • Analyze data related to markets, incentives, and decision‑making.
  • Build models that support policy evaluation and economic forecasting.
  • Communicate evidence‑based insights to academic, industry, or policy audiences.

This major is a strong fit for students interested in economic research, consulting, finance, or graduate study in economics, statistics, or related fields.

  1. Perform a complete and effective analysis of a real data set, choosing appropriate procedures at each step.
  2. Critically assess the strengths and weaknesses of a data analysis and evaluate the validity of the results.
  3. Represent data, code data-driven methods, and competently use a sophisticated statistical computing framework.
  4. Use statistical theory to explain how a statistical methodology works, to identify the assumptions and limitations of a methodology, and to derive a reasonable procedure that solves a statistical problem.
  5. Effectively and clearly communicate economic and statistical methodology and results to specialists and non-specialists -- orally and in writing, and in working cooperatively in multidisciplinary teams.
  6. Describe and illustrate how statistical methods are used to answer questions in disciplines outside of statistics, especially in economics and related disciplines.
  7. Synthesize economic theories and models with data-analytic techniques to (a) provide data-driven solutions to a wide range of social and economic problems, and (b) effectively engage in policy debates.

Details about the EcoStat curriculum, including specific courses required and sample four-year plans, can be found in the Undergraduate Academic Catalog.

Minor in Statistics & Data Science

In order to declare the minor, students need to complete this form.

The StatDS Minor gives students a solid foundation in statistical reasoning and data analysis that complements all majors across CMU.

Students in the minor learn to: 

  • Analyze data using core statistical methods. 
  • Build and interpret statistical models.
  • Use statistical software for data analyses and visualization. 
  • Communicate quantitative findings clearly to technical and non-technical audiences. 

This minor is a great fit for students in any major who want stronger quantitative skills to analyze data and make evidence-based decisions in their field of study. 

Details about the StatDS Minor curriculum, including specific courses required and sample four year plans, can be found in the Undergraduate Academic Catalog.