The program takes place over one academic year and one summer, with the option for an additional pre-program summer for students who lack one or more of the basic prerequisites. The regular program includes two semesters of coursework and a one-summer (anywhere between five and ten weeks) capstone project focused on data analysis in a particular application area.
There are nine credits required to pass the program: four in each of the academic year semesters, and one (the capstone project) in the summer. The nine credits divide as follows:
- 3 credits in mathematical and statistical foundations
- 3 credits in data and computational science
- 1 credit in societal implications and opportunities
- 1 credit in an elective drawn from a wide range of focused applications or deeper theoretical exploration, and
- 1 credit in a capstone project
The first semester consists of two double-credit courses, each counting as two units (six meeting hours per week per course).
- An Introduction to Topics in Probability, Statistics, and Machine Learning 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, and regression and density estimation.
- An Introduction to Data and Computational Science covers basic computational models and algorithms; data management and visualization; basic web programming; information retrieval, integration, and cleaning; hardware; distributed systems; security and privacy; and 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. The project groups formed toward the end of the term work on analyzing data from one of several possible areas of application using the techniques and tools learned in the first-semester courses. The semester concludes with each group giving an oral presentation or hosting a poster session.
The second semester covers four single-credit courses:
- Probability, Statistics and Machine Learning: Advanced Methods includes topics such as estimation and approximation in exponential families, nonparametric regression and density estimation, classification, and ensemble methods.
- Data and Computational Science: Advanced Methods includes topics such as data mining, computational statistics, machine learning and predictive modeling, and big data analytics algorithms.
- Data and Society is a uniquely interdisciplinary course, typical of Brown, with case studies that cover topics such as broader implications in policy and ethics; publication bias and its impacts on society; security versus privacy; and homeland security, the NSA, and the hope for automated triage. This course leverages faculty and curricular existing resources, including the Watson Institute and departments in the social sciences and humanities.
- An elective proposed by the student and approved by the Program Director. Please note that there are a number of existing and new courses outside of the four core departments that could serve as appropriate electives. In these elective courses and their capstone projects, students may choose to apply the skills acquired in the rest of their courses to topics and areas of particular intellectual interest.
For their 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, although students may collaborate with an additional faculty member, postdoc, or industry partner on their project. Each student prepares a paper and/or oral presentation of their work. The project should entail at least 180 hours of work (to receive one course credit) and as such, may be completed in five to ten weeks. The capstone may begin and end at any time during the summer, and a letter grade is awarded for it. Upon completion, students receive a certification of completion of course requirements for the ScM degree, although the actual degree is not officially awarded until the following May.
Pre-Program Summer (As Applicable)
In order to cover missing prerequisites, we offer courses during the Brown summer session. Students needing this background preparation enroll through the usual channels. Note that these summer courses are prerequisites only and don't count toward the Master’s degree requirements. Students taking Brown courses in the summer incur additional tuition costs. Students admitted to the Master’s program may also complete their prerequisite coursework at another institution with approval from the Program Director.