Courses: MSDS students are required to satisfy the following required 36 credits of coursework:
Introduction to Data Science, DS-GA 1001, 3 credits
Probability and Statistics for Data Science, DS-GA 1002, 3 credits
Machine Learning and Computational Statistics, DS-GA 1003, 3 credits
Big Data, DS-GA 1004, 3 credits
Capstone Project, DS-GA 1006, 3 credits
One Data Science Elective, 3 credits
General Electives, 18 points
The Data Science Elective course is chosen from a list of core courses approved and reviewed annually by the curriculum committee. The list may be found here.
In addition to the list of pre-approved elective courses any student may request approval from the DGS to have a particular course approved as a General Elective. The current list of pre-approved general electives may be found here.
Capstone: The purpose of the capstone project is to make the theoretical knowledge acquired by students operational in realistic settings. During the project, students see through the entire process of solving a real-world problem: from collecting and processing real-world data, to designing the best method to solve the problem, and implementing a solution. The problems and datasets come from real-world settings identical to what the student would encounter in industry, academia, or government. Students work individually or in small groups on a problem that typically comes from industry and involves an industry-sourced data set. A list of such problems will be available early in the semester and students should select a problem aligned with their personal interests. Students with similar interests may form groups. The selection of problems to work and the formation of the groups must be approved by the program director.
Concentration in Industry
This concentration is specifically targeted to respond to the needs and inputs from companies and allows MS in Data Science students to apply the knowledge and skills obtained in their coursework to industry during the degree program. It requires more industry-targeted coursework and a Practical Training experience. In addition to the courses required for the M.S., students in this concentration will be required to take the following courses for the degree as a part of the 36 credit requirement: Practical Training for Data Science, DS-GA 1009, within the first year of the program (3 credits in fall, spring, or summer) and 2 electives within the Big Data or Natural Language Processing subject areas (6 credits, see here for more details). The general elective requirement is therefore reduced to 9 credits.