Master of Science in Data Science
The Data Science Institute (DSI) at Brown offers a master's program (ScM) that prepares students from a wide range of disciplinary backgrounds for distinctive careers in Data Science. With connections to departments across campus, in particular Brown's Division of Applied Mathematics and Department of Computer Science, the master's program offers a unique and rigorous education for people building careers in data science. The program empowers students to work through data science projects from formulation, data collection, exploratory data analysis, model development and evaluation to deployment and communicating results with technical and non-technical audiences. Students have the opportunity to learn about various methods and algorithms in data science including but not limited to statistical methods, machine learning, deep learning, generative and agentic AI. The program also provides experience in important, frontline data-science problems in a variety of fields, and introduces students to ethical and societal considerations surrounding data science and its applications.
The program's course structure, including the capstone experience, ensures that students meet the goals of acquiring and integrating foundational knowledge for data science, applying this understanding in relation to specific problems, and appreciating the broader ramifications of data-driven approaches to human activity.
Most students begin the program in the fall semester, which starts in early September. Students may also begin in the spring semester, which starts in late January. The default program length is 21 months for students who start in the fall and 24 months for students who start in the spring. Students who start in the fall may choose to complete the program in 12, 16, 21, or 24 months. Students who start in the spring may choose to complete the program in 16, 21, or 24 months; the 12-month option is not available for spring starts.
The curriculum for the Data Science Master's Program consists of nine credits: four core courses (this includes the capstone experience), four courses chosen from a list of restricted electives, and one unrestricted elective.
Exceptionally well-prepared students may be permitted to substitute additional restricted electives for some core courses, with the exception of DATA 2050. Substitutions are approved by the Director of Graduate Studies and the instructors of the core courses on a case-by-case basis. Decisions can be based on the syllabi of comparable courses completed previously and corresponding grade thresholds or a proficiency exam.
In addition, students choose four restricted electives from the approved course list below. The courses are grouped into categories to help students identify options aligned with their interests. At least one course needs to be selected from the Responsible Data Science category to ensure students are exposed to ethics in data science. Students may select the other three courses from the full list. Note that overrides for non-DATA courses can be difficult to obtain. Pre-registration is highly recommended! The list will be updated regularly by the Director of Graduate Studies to reflect changes in course offerings. Courses will be added upon departmental or instructor approval.
Students may complete one unrestricted elective. This elective may be any graduate-level course that offers domain knowledge relevant to the student’s individual interests. To qualify, the course’s four-digit course number must begin with a nonzero digit and it needs to be completed for a letter grade, not the SNC grade option. Students may select a course from the restricted-electives list as their unrestricted elective. Students considering a course outside the DSI, Computer Science and Applied Mathematics Departments are advised to consult the Director of Graduate Studies before enrolling.
While the open curriculum offers flexibility and student choice, we also recognize that some may find it difficult to navigate the course selection process. To that end, we suggest focus areas, which are oriented toward various data science roles and domains (like Data Analyst, General Data Scientist, Machine Learning Engineer, Public Good Data Scientist, Computational Biology / Genomics Data Scientist, Healthcare Analytics / Public Health Data Science). More information is provided on the program's website.
We also offer the option of a 5th Year Master's Program if you are an undergraduate at Brown. This allows you to substitute maximally 2 credits with courses you have already taken. 5th-Year students must complete their Master's degree in one year (September - August).
For more information on admission and program requirements, please visit the following website: https://graduateprograms.brown.edu/graduate-program/data-science-scm.
Master of Science in Data Science
For more information about the Master's Program curriculum and when courses are offered, please visit the DSI Master's curriculum page or Courses@Brown.
| Core Course Requirements | ||
| DATA 1030 | Hands-on Data Science | 1 |
| DATA 1050 | Data Engineering | 1 |
| DATA 2010 | Math in Machine Learning | 1 |
| DATA 2050 | Data Science Practicum | 1 |
The practicum experience is a hands-on thesis project that entails an in-depth study of a current problem in data science. Students will synthesize their knowledge of probability and statistics, machine learning, and data and computational science. Students will work in teams on projects with Brown faculty members or with external companies. The project will be completed as part of a course that includes additional career-oriented skills development. | ||
| Restricted electives: | 4 | |
| Responsible Data Science (at least one course required): | ||
| Fairness in Automated Decision Making | ||
| Artificial Intelligence Law and Policy | ||
CSCI 2953B | So You Want To Govern AI? | |
| Sociotechnical Approaches to AI and HCI | ||
| Deep learning and GenAI: | ||
| Machine Learning: from Theory to Algorithms | ||
or CSCI 1420 | Machine Learning | |
DATA 1954S | Tech, Data,& Author | |
| Deep Learning | ||
or CSCI 2470 | Deep Learning | |
| Statistics and Applied Math: | ||
| Introduction to Probability and Statistics with Calculus | ||
| Statistical Learning | ||
| Computational Probability and Statistics | ||
| Using R for Data Analysis | ||
| Practical Data Analysis | ||
| Optimization Algorithms in Data Science | ||
| Causal Inference and Missing Data | ||
| Bayesian Statistical Methods | ||
| Design of Experiments | ||
| Applied Longitudinal Data Analysis Half credit course, take with STAT 2517 | ||
| Applied Multilevel Data Analysis Half credit course, take with STAT 2516 | ||
| Public Health and Biostatistics: | ||
| Survey of Health Informatics | ||
| Artificial Intelligence in Health Care | ||
| Computational Biology: | ||
| Inference in Genomics and Molecular Biology | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Pathogenomics: Analysis, interpretation and applications of microbial genomes | ||
| Computational Molecular Biology | ||
or CSCI 2810 | Advanced Computational Molecular Biology | |
| Algorithmic Foundations of Computational Biology | ||
or CSCI 2820 | Algorithmic Foundations of Computational Biology | |
| Computational Cognitive Neuroscience | ||
| Other: | ||
| Data Visualization & Narrative | ||
| The Entrepreneurial Process | ||
| Tackling Climate Change with Machine Learning | ||
| Research in Data Science | ||
| Unrestricted elective | 1 | |
Domain knowledge relevant to individual interest, 1 credit, must be a graduate level course with 4-digit course number starting with a non-0 digit. Most graduate level CSCI and APMA courses qualify. All courses on the restricted electives list qualify as well. Please contact the DGS if you plan to take a course from a different department. | ||
| Total Credits | 9 | |