The program will be conducted over one academic year plus 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 (5- 10 week) capstone project focused on data analysis in a particular application area.
There are nine credits unites required to pass the program: four in each of the academic year semesters, and one (the capstone experience) in the summer. The nine credit-units 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 elective credit to be drawn from a wide range of focused applications or deepertheoretical exploration, and
- 1 credit capstone experience.
The first semester will consist 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: This course will include 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.
- An Introduction to Data and Computational Science: The course will cover 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 will be closely coordinated and will 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 will 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 will conclude 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; ensemble methods;
- Data and Computational Science: Advanced Methods: Includes topics such as data mining; computational statistics; machine learning and predictive modeling; big data analytics algorithms;
- Data and Society: A uniquely Brown course involving case studies that will cover topics such as the broader implications in policy and ethics; publication bias and its impacts on society; security vs. privacy; and homeland security, NSA, and the hope for automated triage. This course will leverage faculty and curricular existing resources, including the Watson Institute and departments in the social sciences and humanities;
- Elective: The elective course will be proposed by the student and approved by the program director. Please note that a number of both existing and new courses that would be appropriate electives fall outside of the four core departments. Students may choose, in these elective courses and in their capstone projects, to apply the skills acquired in the rest of their courses to topics and areas of particular intellectual interest.
For their capstone experience, students will work on a project with real data, potentially in any one of the areas covered by the elective course. A faculty member from one of the four departments will oversee the capstone course, although each student may collaborate with an additional faculty member, postdoc, or industry partner on his/her project. Each student will prepare a paper and/or oral presentation of his/her 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 pre-requisites 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 of the program director.