Master’s in Data Science: Industry Concentration

On this page: Required Courses • Big Data • Natural Language Processing (NLP)

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CDS offers the Industry Concentration for the MS in Data Science. This concentration is specifically designed to respond to the needs and inputs from companies, allowing MS in Data Science students to apply the knowledge and skills obtained in their coursework to industry-related projects during the degree program. It requires more industry-targeted coursework and a Practical Training experience, including a mandatory internship within the first year of study. International students should consult NYU’s Office of Global Services on how to obtain early CPT status within their first year if pursuing this concentration. 

Required Courses

Students in this concentration will be required to take the following courses as part of the 36-credit requirement:

  • DS-GA 1009: Practical Training for Data Science within the first year of the program (3 credits in fall, spring, or summer)
  • 2 electives within the Big Data or Natural Language Processing subject areas (6 credits, see below for more details)

Big Data

The courses below fall within the Big Data subject area. This list is approved and reviewed annually by the curriculum committee:

  • DS-GA 1012: Natural Language Understanding and Computational Semantics
  • DS-GA / CSCI-GA 2433: Database Systems
  • CS-GY 6083 Principles of Database Systems
  • CS-GY 6093: Advanced Database Systems
  • CS-GY 6313: Information Visualization
  • CS-GY 6323: Large-Scale Visual Analytics
  • CSCI-GA 2434: Advanced Database Systems
  • CSCI-GA 2436: Realtime and Big Data Analytics
  • CSCI-GA 2437: Big Data Application Development
  • CSCI-GA 3033: Cloud and Machine Learning
  • CSCI-GA 3033: Introduction to Deep Learning Systems
  • INTG1-GC 1025: Database Management & Modeling
  • MATH-GA 2047: Trends in Financial Data Science
  • TECH-GB 2350: Robo Advisors & Systematic Trading

Natural Language Processing (NLP)

The courses below fall within the Natural Language Processing subject area. This list is approved and reviewed annually by the curriculum committee.

  • DS-GA 1005: Inference and Representation
  • DS-GA 1008: / CSCI-GA 2572 Deep Learning
  • DS-GA 1011: Natural Language Processing with Representation Learning
  • DS-GA 1012: Natural Language Understanding and Computational Semantics
  • DS-GA 1015: Text as Data
  • CSCI-GA 2590: Natural Language Processing
  • CSCI-GA 3033: Learning with Large Language and Vision Models
  • CSCI-GA 3033: Statistical NLP

All other requirements remain the same. For more information on the MS in Data Science curriculum, see the MS Curriculum page.

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