Required Course Information

Students must successfully complete the following five courses by the end of their third semester, or show evidence that they have taken equivalent coursework elsewhere. Recent course pages are linked below. Course descriptions can be found in NYU’s Albert Course Search.

Elective Courses

Students must successfully complete 57 credit hours of elective courses. Faculty at the Center for Data Science are experts in a broad range of data science topics, and the Center’s course offerings reflect that diversity. For example, students will be able to take courses in Deep Learning, Optimization, and Natural Language Processing.

Some of the pre-approved courses are below. Please see NYU’s Albert Course Search for course descriptions.

  • Deep Learning (DS-GA 1008)
  • Optimization-based Data Analysis (DS-GA 1013)
  • Mathematics of Data Science (MATH-GA 2830)
  • Natural Language Understanding with Distributed Representations (DS-GA 1012)
  • Research Rotation Courses: A research rotation is a semester-long guided research experience in which the student will have an opportunity to design and carry out original research in a collaborative setting. The idea is to help students identify research interests. PhD students normally take this elective 6 times.
  • Preparation for Teaching Data Science: In this class, students learn effective teaching skills for teaching data science topics to university students. They will help prepare and deliver an assigned course.
  • Practical Training for Data Science (DS-GA 1009): Practical Training offers course credit for academically relevant internship experience. This is an integral part of the PhD Program curriculum and facilitates students academic and professional development. The course allows students to apply their academic and research knowledge to real-world problems.

Students can take courses that are not on the pre-approved course list with permission from the Director of Graduate Studies (DGS).