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Brown University’s Master of Science (ScM) in Data Science is a highly rigorous, research-driven, and STEM-designated graduate program that prepares students for both industry and academic careers in data science, artificial intelligence, and statistical computing. Offered by the Department of Computer Science in close collaboration with Biostatistics and Applied Mathematics, the program emphasizes algorithmic depth, statistical reasoning, data ethics, and interdisciplinary application.
Located in Providence, Rhode Island, and part of the Ivy League, Brown’s open academic culture and strong research ecosystem foster a collaborative, innovative approach to real-world problem-solving in data science.
Program Format & Duration
Full-time, residential program (typically completed in 16 months)
STEM-designated (eligible for OPT extension for international students)
Option to pursue a research-based or professional capstone project
Fall start only
Core Curriculum Components
The Brown ScM in Data Science consists of 8 graduate-level courses and a capstone or thesis, offering strong theoretical foundations and practical skills.
Required Core Courses
CSCI 1951A: Data Science
CSCI 1420: Machine Learning
CSCI 2000-level elective: Advanced Computer Science course
APMA 1650 or 1655: Statistical Inference
CSCI 1951R: Ethics and Impact of Data Science
Elective Options
Students choose electives from departments such as:
Computer Science
Biostatistics (School of Public Health)
Applied Math
Economics
Engineering
Sample topics include:
Deep Learning
Bayesian Inference
NLP and Text Mining
Data Visualization
Genomics and Bioinformatics
Computational Social Science
Capstone or Thesis
Students complete one of the following:
Professional Capstone Project: Team or individual project using real datasets, often in collaboration with industry, nonprofits, or Brown research labs
Master’s Thesis: For students interested in deeper academic research or pursuing a PhD
Brown’s ScM in Data Science strongly emphasizes applied learning, ethical reasoning, and interdisciplinary collaboration. Students engage with research and real-world data through coursework, capstone projects, and optional internships.
Capstone Project
Capstone projects allow students to:
Partner with industry clients, university research centers, or nonprofit organizations
Solve real-world problems using predictive modeling, statistical analysis, or AI/ML methods
Present final deliverables to faculty and external partners
Research Opportunities
Students may collaborate with:
Brown’s Center for Computational Molecular Biology
Data Science Initiative (DSI)
School of Public Health (Biostatistics)
Institute for Computational and Experimental Research in Mathematics (ICERM)
AI Lab and Social Analytics groups in Computer Science
Research topics include:
Fairness in machine learning
High-dimensional biological data
Environmental data modeling
Urban systems and mobility
Health analytics and epidemiological modeling
Technical Tools & Platforms
Students gain hands-on expertise in:
Python, R, SQL
TensorFlow, Keras, PyTorch
Spark, Hadoop, AWS/GCP
Git, Docker, Jupyter
D3.js, Tableau, Seaborn
Brown ScM in Data Science graduates are known for their research-readiness, ethical awareness, and interdisciplinary agility. Whether in tech, health, academia, or public policy, alumni bring both analytical power and human-centered thinking to complex data problems.
Career Outcomes
Graduates pursue roles such as:
Hiring Industries
Graduate & Research Pathways
Salary Overview-
Top Employers-
Geographic Placement-
Industry Sectors-
Career Growth and Progression-
What This Means for You-
Bachelor’s Degree: Earn a bachelor’s degree from an accredited institution. Prerequisite Coursework: Complete at least one semester of calculus, one semester of linear algebra, one semester of calculus-based probability & statistics, and an introductory programming course (with familiarity in Python or R).Minimum GPA: Maintain a minimum undergraduate GPA of 3.0 on a 4.0 scale (exceptional cases may be considered).Transcripts: Upload unofficial transcripts with your application; admitted students must then submit official transcripts (and certified translations if needed).English Proficiency: If applicable, submit TOEFL or IELTS scores (unless you have an English-instruction degree); specific country exemptions apply.GRE Scores: GRE General and Subject tests are not required and will not be considered.Letters of Recommendation: Provide three letters of recommendation.Application Fee & Waiver: Pay the application fee or request a waiver via the online form before the deadline.GRE Score Range: The GRE is not required for Brown’s ScM in Data Science, and applications are evaluated holistically without consideration of GRE scores anymoreThree‑Year Bachelor's Degree: Brown accepts three-year undergraduate degrees from countries where that is the norm WES Evaluation: An international transcript evaluation (e.g., WES, ECE) is required before enrollment for all non‑U.S. degrees, though not required at application timeData AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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