Curriculum

The curriculum is 36 credits, half of which are required courses and half of which are electives. One of the key features of the MS in Data Science curriculum is a capstone project that makes the theoretical knowledge you gain in the program operational in realistic settings. During the project, you will go 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 finally, to implementing a solution. The problems and datasets you’ll engage with will come from real-world settings identical to what you might encounter in industry, academia, or government.

4 Semester Option

Year 1 – Fall

Course Title Credits
TOTAL CREDITS 9
DS-GA-1001  Intro to Data Science 3
DS-GA-1002 Statistical and Mathematical Methods for Data Science 3
Data Science Elective 1 3

Year 1 – Spring

Course Title Credits
TOTAL CREDITS 9
DS-GA-1003 Machine Learning and Computational Statistics 3
DS-GA-1004 Big Data 3
Data Science Elective 2 3

Year 2 – Fall

Course Title Credits
TOTAL CREDITS 9
DS-GA-1005 Inference and Representation 3
DS-GA-1006 Capstone Project in Data Science 3
Data Science Elective 3 3

Year 2 – Spring

Course Title Credits
TOTAL CREDITS 9
Data Science Elective 4 3
Data Science Elective 5 3
Data Science Elective 6 3

3 Semester Option

Year 1 – Fall

Course Title Credits
TOTAL CREDITS 15
DS-GA-1001  Intro to Data Science 3
DS-GA-1002 Statistical and Mathematical Methods for Data Science 3
Data Science Elective 1 3
Data Science Elective 2 3
Data Science Elective 3 3

Year 1 – Spring

Course Title Credits
TOTAL CREDITS 15
DS-GA-1003 Machine Learning and Computational Statistics 3
DS-GA-1004 Big Data 3
Data Science Elective 4 3
Data Science Elective 5 3
Data Science Elective 6 3

Year 2 – Fall

Course Title Credits
TOTAL CREDITS 6
DS-GA-1005 Inference and Representation 3
DS-GA-1006 Capstone Project in Data Science 3