Master’s in Data Science

Curriculum

Degree Requirements for the MS in Data Science can be found in the GSAS bulletin – Master of Data Science.

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 the industry, academia, or government.

The MSDS allows students to pursue the Industry Concentration, which allows them to apply the knowledge and skills obtained in their coursework to industry during the degree program.

The MSDS also offers students the opportunity to pursue a track. Tracks allow you to connect with faculty who advise and help you plan your curriculum and career goals. 

For more information on the MS curriculum and requirements please visit the MS Student Handbook. Please note you will only be able to access the handbook through your NYU email address.

Required Course Information

Course descriptions can be found in NYU’s Albert Course Search. Recent course pages are linked below.

  1. DS-GA 1001 Introduction to Data Science
  2. DS-GA 1002 Probability and Statistics for Data Science
  3. DS-GA 1003 Machine Learning
  4. DS-GA 1004 Big Data
  5. DS-GA 1006 Capstone Project and Presentation
  6. One Data Science Elective (choose 1 from the list below).
    1. DS-GA 1005 Inference and Representation
    2. DS-GA 1008 Deep Learning
    3. DS-GA 1011 Natural Language Processing with Representation Learning
    4. DS-GA 1012 Natural Language Understanding and Computational Semantics
    5. DS-GA 1013 Mathematical Tools for Data Science
    6. DS-GA 1014 Optimization and Computational Linear Algebra
    7. DS-GA 1015 Text as Data
    8. DS-GA 1016 Computational Cognitive Modeling
    9. DS-GA 1017 Responsible Data Science
    10. DS-GA 1018 Probabilistic Time Series Analysis

Electives Information

In addition to the required courses and track electives, MS in Data Science students will fulfill their remaining credits with general electives. CDS students can receive general elective credit for any class listed with a course code that begins with DS-GA as long as the course is not being used to fulfill one of their required courses and is not offered for only non-MS in Data Science students.

In Spring 2022, CDS offered the electives below.

In Fall 2022, CDS offered the electives below.

In Spring 2023, CDS is offering the electives below.

Students may also take one of the pre-approved non-DS electives listed here as general elective credits towards their MSDS degree. However, please note that these courses may have prerequisites or registration restrictions. Students will need to consult the sponsor department website for such details. Often a search on “NYU” + the course name will lead to details for the course.

Students wishing to take elective courses not on the list need to obtain approval. For approval, please complete the Elective Request for DS Students Google form. You will be asked for the below information.

  • Course information
  • Course syllabus
  • The relevance of course to MSDS degree and goals
  • If available, the course website

If you have any questions regarding the eligibility of a course to serve as a general elective please contact Tina Lam at tina.lam@nyu.edu.

4 Semester Course Plan

This is just a guide to help plan out courses for the degree. The course plan may vary based on when courses are offered.

Year 1 – Fall

Course TitleCredits
DS-GA-1001 Intro to Data Science3
DS-GA 1002 Probability and Statistics for Data Science3
General Elective or Track Course3
TOTAL CREDITS9

Year 1 – Spring

Course TitleCredits
DS-GA-1003 Machine Learning3
DS-GA-1004 Big Data3
General Elective or Track Course3
TOTAL CREDITS9

Year 2 – Fall

Course TitleCredits
Data Science Elective (see list in required courses section)3
DS-GA-1006 Capstone Project in Data Science3
General Elective or Track Course3
TOTAL CREDITS9

Year 2 – Spring

Course TitleCredits
General Elective or Track Course3
General Elective or Track Course3
General Elective or Track Course3
TOTAL CREDITS9