Curriculum

The curriculum for the MS in Data Science (MSDS) degree is 36 credits. 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.

The MSDS also offers two ways to structure the graduate program that give students the opportunity to pursue a specialization.

  1. Students in the Data Science track will follow the standard curriculum, which consists of 6 required courses and 6 electives.
  2. Students may also enroll in tracks where specializations are formalized. In addition to the required courses, students will have to fulfill track requirements as part of their electives. The track descriptions are listed below. Upon completing the track requirements, in addition to the MSDS degree, the student will receive a certificate from the Center for Data Science.

Data Science Track

In the Data Science track, students take six required courses and six elective courses from a diverse list of courses.

Data Science Big Data Track

The Data Science Big Data track focuses on methods and techniques required to acquire, manage, analyze and visualize large volumes of data. Student will acquire deep understanding of algorithms and their complexity and gain hands-on experience on how to build end-to-end solutions to computational problems.

Data Science Mathematics and Data Track

The Data Science Mathematics and Data track provides the mathematical background to understand and analyze modern data-analysis methods in areas such as deep learning, compressed sensing, high-dimensional statistics and graph signal processing. In addition, the track will provide exposure to fundamental research problems inspired by newly-developed data-science techniques.

Data Science Natural Language Processing Track

The Data Science Natural Language Processing Track will give students the skills to build machine learning models that can understand, manipulate, or produce data expressed in natural language text.

Data Science Physics Track

The Data Science Physics Track provides the same solid foundation in data science and further develops modeling and inference skills in the context of compelling, data-intensive physics research topics. This track is ideal for applicants who have some physics background, are interested in transitioning into a career in data science, and wish to leverage those skills for a competitive advantage.

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