Courses: MSDS students are required to satisfy the following required 36 points of coursework:
Introduction to Data Science, DS-GA 1001, 3 points
Probability and Statistics for Data Science, DS-GA 1002, 3 points
Machine Learning and Computational Statistics, DS-GA 1003, 3 points
Big Data, DS-GA 1004, 3 points
Capstone Project, DS-GA 1006, 3 points
One Data Science Elective, 3 points
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.