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 (For students starting program in Fall 2017 or later)

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 (link is to syllabus of Spring 2020 course offering)
  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. Fall 2020 DS-GA 1170/CSCI-GA 1170 Fundamental Algorithms (only the Fall 2020 course offering will fulfill the requirement)
    8. Fall 2020 DS-GA 2433/CSCI-GA 2433 Database Systems (only the Fall 2020 course offering will fulfill the requirement)
    9. Fall 2020 CSCI-GA 2110 Programming Languages (only the Fall 2020 course offering will fulfill the requirement)

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 Fall 2020, CDS offered the electives below.

  • DS-GA 1007 Programming for Data Science
  • DS-GA 1009 Practical Training for Data Science
  • DS-GA 1010 Independent Study
  • DS-GA 3001 Special Topics in Data Science: Probabilistic Time Series Analysis
  • DS-GA 3001 Special Topics in Data Science: Risk Management & Machine Learning
  • DS-GA 3001 Special Topics in Data Science: Machine Learning for Healthcare
  • DS-GA 3001 Special Topics in Data Science: Mathematical Statistics
  • DS-GA 3001 Special Topics in Data Science: Bayesian Machine Learning
  • DS-GA 3001 Special Topics in Data Science: Introduction to Data Science for PhD Students
  • DS-GA 3001 Special Topics in Data Science: Search and Discovery

In Spring 2021, CDS is offering the electives below.

  • DS-GA 1009 Practical Training for Data Science
  • DS-GA 1010 Independent Study
  • DS-GA 1015 Text as Data
  • DS-GA 1016 Computational Cognitive Modeling
  • DS-GA 1017 Responsible Data Science
  • DS-GA 3001 Special Topics in Data Science: Advanced Python for Data Science
  • DS-GA 3001 Special Topics in Data Science: Introduction to Computer Vision
  • DS-GA 3001 Special Topics in Data Science: Tools and Techniques for Machine Learning
  • DS-GA 3001 Special Topics in Data Science: Predictive Modeling with Sports Data

Students can also take one of the pre-approved non-DS electives listed below. If you have any questions regarding the eligibility of a course to serve as a general elective please contact our Academic Affairs Program Administrator Elisa Fox at elisa.fox@nyu.edu.

Pre-approved Non-CDS General Elective Courses Starting Spring 2018

Below is a list of non-Center for Data Science electives that are pre-approved for Data Science graduate students starting in Spring 2018. 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, please contact Elisa Fox at elisa.fox@nyu.edu.

Note that some courses have prerequisites, which are not listed. You will need to consult the department website for such details. Often a search on “NYU” + the course name will lead to details for the course.

  • CSCI-GA 1170 Fundamental Algorithms
  • CSCI-GA 2433 Database Systems
  • MATH-GA 2751 Risk & Portfolio Management with Econometrics
  • CSCI-GA 2566 Foundations of Machine Learning
  • BIOL-GA 1127/CSCI-GA 2520 Bioinformatics & Genomes
  • CSCI-GA 3033 Special Topics Computer Science: Statistical Natural Language Processing
  • CS-GY 6003 Foundations of Computer Science
  • STAT-GB 2302 Forecast Time Series Data
  • STAT-GB 3383 Frequency Domain Time Series
  • STAT-GB 2301 Regression & Multivariate Data Analysis

Pre-approved General Elective Courses For Fall 2017 – After Fall 2017, This List Will No Longer Be Valid

A list of electives that were pre-approved for Data Science graduate students in Fall 2017 and prior semesters can be found here. This list is no longer valid. 

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 Learning and Computational Statistics3
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