Academic Programs Master's in Data Science

The Master’s Program in Data Science (ScM) prepares students from a wide range of disciplinary backgrounds for distinctive careers in data science. Rooted in a research collaboration between four very strong academic departments (Applied MathematicsBiostatisticsComputer Science, and Mathematics), the master's program offers a unique and rigorous education for people building careers in data science and/or big data management.

The program is designed to provide a fundamental understanding of the methods and algorithms of data science, to be achieved through a study of relevant topics in mathematics, statistics, and computer science, including machine learning, data mining, security and privacy, visualization, and data management. 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 (see below) 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. Our strong industry partnerships help students learn about industry's needs and directions, and provide novel and unique opportunities to work with data.

For Brown undergraduates, this can be a 5th-year master's program. This option allows substitution of at most 2 credits with courses already taken as undergraduate student.

Program Structure

The program is conducted over one academic year plus one summer. The regular program includes two semesters of coursework and a 5-10 week capstone project focused on data analysis in a particular application area. Pre-program summer classes are available for students who lack one or more of the basic prerequisites.

Nine credit-units are required to pass the program: four in each of the academic year semesters, and one (the capstone experience) in the summer:

  • 3 credits in mathematical and statistical foundations
  • 3 credits in data and computational science
  • 1 credit in societal implications and opportunities
  • elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration
  • 1 credit capstone experience

Semester 1 (2 double-credit courses; 6 meeting hours per week per course)

  1. DATA 1010 (2 credits) - An Introduction to Topics in Probability, Statistics, and Machine Learning: This course includes topics such as maximum likelihood estimation (MLE); entropy; divergence; random numbers and their applications; introduction to high-dimensional data; graphical models and exponential families; regression; and density estimation.
  2. DATA 1030 (2 credits) - An Introduction to Data and Computational Science: The course covers basic computational models and algorithms; data management and visualization; basic web programming; information retrieval; integration, and cleaning; hardware; distributed systems; security and privacy; multi-media analytics.

These two courses are closely coordinated and come together in the final weeks through small-group projects that draw on the methods learned in both. Students work in groups on a final project analyzing data from one of several possible areas of application using the techniques and tools learned in the first–semester courses.

Semester 2 (4 single-credit courses)

  1. DATA 2020 (1 credit) - Probability, Statistics and Machine Learning: Advanced Methods: Includes topics such as estimation and approximation in exponential families; nonparametric regression and density estimation; classification; ensemble methods.
  2. DATA 2040 (1 credit) - Data and Computational Science: Advanced Methods: Includes topics such as data mining; computational statistics; machine learning and predictive modeling; big data analytics algorithms.
  3. DATA 2080 (1 credit) - Data and Society: A uniquely Brown course that covers topics such as the broader implications of data in policy and ethics; publication bias and its impacts on society; security vs. privacy; and homeland security, NSA, and the hope for automated triage. The course leverages existing faculty and curricular resources, including the Watson Institute and departments in the social sciences and humanities.
  4. Elective (2000-level course) - The elective course will be proposed by the student and approved by the program director. Both existing and new courses outside the four core departments may be appropriate electives. The elective courses and capstone projects give students a chance to apply the skills acquired in the rest of their courses to topics and areas of particular intellectual interest. 

Summer Capstone

DATA 2050, 1 credit – Capstone Project: Students work with real data, potentially in any one of the areas covered by the elective course. A faculty member from one of the four departments oversees the capstone course, although each student may collaborate with an additional faculty member, postdoc, or industry partner on his/her project. Students prepare a paper and/or oral presentation of their work. The summer capstone should entail at least 180 hours of work (to receive one course credit) and as such, may be completed in 5-10 weeks. The project may begin and end at any time during the summer. A letter grade will be awarded for the summer capstone course.

Upon completion of the summer capstone, students will receive a certification of completion of course requirements for the ScM degree, although the actual degree will not be officially awarded until the following May.

Pre-program summer (as applicable)

In order to cover missing prerequisites we will offer courses during the Brown summer session. Students needing this background preparation will enroll through the usual channels. We note that these summer courses are prerequisites only and would not count towards the master’s degree requirements. Students taking Brown courses in the summer will incur additional tuition costs. Students admitted to the master’s program may also complete their prerequisite coursework at another institution, with appropriate approval from the program director