On this page: Required Course Information • Electives Information • 4 Semester Course Information
Degree Requirements for the MS in Data Science can be found in the NYU 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 MSDS curriculum is a capstone project that allows you to apply the theoretical knowledge gained throughout the program to real-world 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 work with will come from real-world settings, mirroring what you might encounter in the industry, academia, or government.
The MSDS offers students the opportunity to pursue the Industry Concentration, which enables them to apply the knowledge and skills obtained in their coursework to industry during the degree program.
Alternatively, the MSDS offers students the option to pursue a “track.” Tracks allow you to connect with faculty who will advise and help you plan your curriculum and career goals.
For more information on the MSDS curriculum and requirements please visit the MS Student Handbook. Please note that 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.
- DS-GA 1001 Introduction to Data Science
- DS-GA 1002 Probability and Statistics for Data Science
- DS-GA 1003 Machine Learning
- DS-GA 1004 Big Data
- DS-GA 1006 Capstone Project and Presentation
- One Data Science Elective (choose 1 from the list below).
- DS-GA 1005 Inference and Representation
- DS-GA 1008 Deep Learning
- DS-GA 1011 Fundamentals of Natural Language Processing (formerly Natural Language Processing with Representation Learning)
- DS-GA 1012 Large Language Models: Evaluaton and Applications (formerly Natural Language Understanding and Computational Semantics)
- DS-GA 1013 Mathematical Tools for Data Science
- DS-GA 1014 Optimization and Computational Linear Algebra
- DS-GA 1015 Text as Data
- DS-GA 1016 Computational Cognitive Modeling
- DS-GA 1017 Responsible Data Science
- DS-GA 1018 Probabilistic Time Series Analysis
- DS-GA 1020 Mathematical Statistics
- DS-GA 1021 Probability and Statistics 2
Electives Information
General electives offered at CDS include the courses below. The courses listed under the “One Data Science Elective Course” requirement can also be used as a general elective, provided they are not fulfilling the “One Data Science Elective Course” requirement.
- DS-GA 1007 Programming for Data Science
- DS-GA 1009 Practical Training for Data Science
- DS-GA 1010 Independent Study
- DS-GA 1019 Advanced Python for Data Science
- DS-GA 3001 Special Topics in Data Science (topics vary each semester; examples of current and previous offerings are listed below).
- Reinforcement Learning
- Introduction to Computer Vision
- Applied Statistics
- Modern Topics in Statistical Learning Theory
- Intro to Applied ML in Finance I: Discrete Choice
- Visualization in Machine Learning
- Causal Inference in Machine Learning
- Mathematics of Deep Learning
Students may also take one of the pre-approved non-DS electives listed in the Pre-approved Non-CDS General Elective Course spreadsheet as general elective credits towards their MSDS degree. However, please note that these courses may have prerequisites or registration restrictions. Students should 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 must 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 your MSDS degree and goals
- The course website (if available)
If you have any questions regarding the eligibility of a course to serve as a general elective, please contact cds-masters@nyu.edu.
4 Semester Course Plan
This is just a guide to help plan out courses for the degree. Your actual course plan may vary based on when courses are offered.
YEAR 1 – FALL
| Course Title | Credits |
|---|---|
| DS-GA-1001 Intro to Data Science | 3 |
| DS-GA 1002 Probability and Statistics for Data Science | 3 |
| General Elective or Track Course | 3 |
| TOTAL CREDITS | 9 |
YEAR 1 – SPRING
| Course Title | Credits |
|---|---|
| DS-GA-1003 Machine Learning | 3 |
| DS-GA-1004 Big Data | 3 |
| General Elective or Track Course | 3 |
| TOTAL CREDITS | 9 |
YEAR 2 – FALL
| Course Title | Credits |
|---|---|
| Data Science Elective (see list in required courses section) | 3 |
| DS-GA-1006 Capstone Project in Data Science | 3 |
| General Elective or Track Course | 3 |
| TOTAL CREDITS | 9 |
YEAR 2 – SPRING
| Course Title | Credits |
|---|---|
| General Elective or Track Course | 3 |
| General Elective or Track Course | 3 |
| General Elective or Track Course | 3 |
| TOTAL CREDITS | 9 |