DATA 1010 (2 credits) - 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. Instructor: Stuart Geman
DATA 1030 (2 credits) - An Introduction to Data and Computational Science: Mastering big data requires skills spanning a variety of disciplines: distributed systems over statistics, machine learning, and a deep understanding of a complex ecosystem of tools and platforms. Data Science refers to the intersection of these skills and how to transform data into actionable knowledge. This course provides an overview of techniques and tools involved and how they work together: SQL and NoSQL solutions for massive data management, basic algorithms for data mining and machine learning, information retrieval techniques, and visualization methods. Instructors: Ugur Cetintemel, Tim Kraska, Dan Potter
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; Instructor: Joe Hogan
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; Instructor: Eli Upfal, Dan Potter
DATA 2080 (1 credit) - 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; Instructor: Jeff Brock, Terry-Ann Craigie
DATA 2050 (1 credit) - Capstone Project: 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 or her project. Each student will prepare a paper and/or oral presentation of his or 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 successful completion of the Capstone Project, 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. Instructor: Dan Potter