BA Statistics and Data Science

2 Years On Campus Bachelors Program

Yale University

Program Overview

The B.A. in Statistics and Data Science at Yale University is designed for students who want to understand how data can be used to make predictions, explain patterns, and support decisions under uncertainty, combining mathematical foundations with computational and practical data-analysis skills. The program suits students interested in applying statistics and data science across areas such as the social sciences, natural sciences, engineering, management, medicine, and digital humanities, with coursework spanning probability, statistics, computation, data-science methods, and application areas.

Curriculum Structure:

Year 1: Students begin by developing their mathematical and introductory data-science foundation, with courses such as S&DS 1230 – YData: An Introduction to Data Science and S&DS 1000 – Introductory Statistics providing computational, programming, statistical reasoning, and data-analysis skills. Students also begin the required multivariable-calculus preparation through MATH 1200 – Calculus of Functions of Several Variables or an approved equivalent.

Year 2: Students build stronger mathematical and computational foundations through the required linear-algebra component, such as MATH 2220, MATH 2250, or MATH 2260, together with courses from the Core Probability and Statistics and Computational Skills areas. Options include S&DS 2410 – Probability Theory and S&DS 2300 – Data Exploration and Analysis, helping students develop the theoretical and practical skills needed for more advanced data-science study.

Year 3: Students progress into dedicated methods of data science and begin applying statistical techniques to different fields. Courses such as S&DS 3120, S&DS 3170, S&DS 3610, or S&DS 3630 allow students to explore increasingly advanced methods, while approved courses in areas such as computer science, economics, psychology, linguistics, or environmental studies can connect data analysis with a specific application domain.

Year 4: Students consolidate their statistical and data-science knowledge through additional approved electives and complete an individual research project through S&DS 4910 – Senior Essay, Fall or S&DS 4920 – Senior Essay, Spring. The senior requirement gives students the opportunity to conduct individual research under the guidance of a faculty adviser and communicate the results through a written project.

Focus Areas: Probability and statistics, computational skills, methods of data science, mathematical foundations and theory, efficient computation and big data, data science in context, and methods in application areas.

Learning Outcomes: Develop the ability to reason about uncertainty using probability and statistics, compute with and analyze data, apply data-science methods, visualize and explore data, identify structures and trends, and use quantitative evidence to support decisions and explanations.

Professional Alignment (Accreditation): Not stated: Yale's official B.A. Statistics and Data Science program and catalog pages do not state professional accreditation for the degree.

Reputation (Employability Rankings): Official Yale employability ranking for this specific B.A. is not stated. Yale's Office of Career Strategy reports that 95.3% of the Yale College Class of 2025 was employed or in graduate school within six months, while the mean starting salary among graduates employed full-time in the United States was $94,028; these figures are Yale College-wide rather than specific to Statistics and Data Science.

Experiential Learning (Research, Projects, Internships etc.)

Students in Yale’s B.A. in Statistics and Data Science develop practical skills by working with real datasets, applying statistical methods, programming, data visualization, and machine learning throughout the major. The program places particular emphasis on computational practice through S&DS 2620 – Computational Tools for Data Science, S&DS 2650 – Introductory Machine Learning, and S&DS 4250 – Statistical Case Studies, where students work with potentially large and messy datasets, develop reproducible R code, and independently compare analytical approaches.

Practical learning opportunities and facilities include:

Statistical Case Studies: S&DS 4250 functions as a guided data-analysis practicum in which students work through practical issues such as acquiring data, evaluating data quality, handling large datasets, combining programming packages, interpreting ambiguous results, and producing readable and reproducible R code.
R and Python: Students gain experience with both R and Python. R is particularly common in data analysis, statistical inference, econometrics, and causal inference, while Python is increasingly used in S&DS 2620 and machine-learning courses such as S&DS 2650 and S&DS 3650.
Senior Research Project: The required S&DS 4910/4920 Senior Essay gives students an opportunity to conduct an independent applied, computational, or theoretical project under faculty mentorship, producing a project proposal, substantial written report, and research poster.
Data Science Project Match: Yale’s Institute for Foundations of Data Science (FDS) organizes Data Science Project Match events where faculty present potential data-science research projects and students can connect with researchers to identify projects and advisors.
Research Across Disciplines: Undergraduate students can seek research opportunities with Statistics & Data Science faculty and affiliated researchers, as well as researchers in Computer Science, Political Science, Economics, Global Affairs, Public Health, Biostatistics, and Management, reflecting the interdisciplinary nature of modern data science.
Real-World Data Competitions: Yale S&DS students participate in ASA DataFest, where teams of 3–5 students analyze a large, unfamiliar dataset and present their findings to judges. In 2026, a Yale team used the R Shiny package to build interactive web applications for exploring and communicating their analysis and won the Best Business Value award.
Institute for Foundations of Data Science: The Institute for Foundations of Data Science brings together data-science research at Yale and provides an important research environment for students interested in statistics, machine learning, computational methods, and interdisciplinary applications.
Kline Tower: The Department of Statistics & Data Science is based in Kline Tower, which includes state-of-the-art classrooms, lounges, meeting spaces, seminar facilities, and workshops designed to support instruction and interaction between faculty and students.

Progression & Future Opportunities

Graduates of Yale University’s B.A. in Statistics and Data Science develop a strong foundation in statistical reasoning, computational methods, data analysis, and data-driven decision-making, preparing them for careers across technology, finance, healthcare, consulting, research, and other data-intensive fields. The program also provides a strong platform for graduate study, while Yale’s Office of Career Strategy supports students with career advising, internships, employer connections, recruiting events, and graduate-school guidance.

Typical Job Roles: Data Analyst, Statistical Analyst, Data Scientist, Business Intelligence Analyst.

Career Development & Employment Opportunities: Yale provides several university-wide services and career pathways that can help Statistics and Data Science students translate their quantitative training into employment:

Career Services: Yale’s Office of Career Strategy (OCS) provides career advising, employer engagement, career fairs, networking events, interview preparation, internship support, and graduate/professional-school advising.
Yale Career Link: Students can search and apply for internships and full-time positions, research thousands of employers and contacts, schedule appointments with OCS advisors, attend employer events, access peer networking lists, and practise interviews through Big Interview.
Industry & Employer Connections: Yale Career Link provides access to Preferred Yale Partners, a network of Yale alumni and employers offering opportunities ranging from short-term internships and project-based work to full-time positions across industries.
Employment & Salary: For the Yale College Class of 2025, 95.3% were employed or in graduate school within six months of graduation, and the mean starting salary among graduates employed full-time in the United States was $94,028. These are Yale College-wide figures rather than Statistics and Data Science-specific figures.
Programming & Data Science Outcomes: Yale’s Office of Career Strategy specifically maintains a Programming & Data Science career community. For the Class of 2025, this community reports 95.3% employed or in graduate school within six months and a $94,028 mean starting salary for graduates employed full-time in the United States; the figures are community-wide rather than specific to the S&DS major.
Research Experience: The department encourages undergraduate research through faculty in Statistics and Data Science and related areas including Computer Science, Economics, Political Science, Global Affairs, Public Health, Biostatistics, and Management. Students can also complete the S&DS 4910/4920 senior project as an independent research project under faculty mentorship.
Graduation Outcomes: Yale’s S&DS B.A. develops fundamental statistical and data-science techniques, while Yale reports that the broader Class of 2025 had 61.0% working full-time and 17.5% attending graduate or professional school six months after graduation.
Long-Term Accreditation Value: No program-specific professional accreditation is stated by Yale for the B.A. in Statistics and Data Science. The degree’s value instead comes from Yale College’s academic framework, the department’s mathematical and computational curriculum, and preparation for professional employment or advanced study.

Further Academic Progression: After completing the B.A., students can pursue graduate study in statistics, data science, computer science, mathematics, economics, biostatistics, public health, or related quantitative fields. Yale’s Department of Statistics and Data Science offers a terminal M.S. in Statistics and Data Science, a terminal M.A. in Statistics, and a Ph.D. in Statistics and Data Science; exceptionally strong Yale undergraduates may also be eligible to structure their studies for a simultaneous bachelor’s/master’s degree.