Degree Requirements

Degree requirements for the PhD in Data Science can be found at

To be awarded the PhD in Data Science, students must, within 10 years of first enrolling:

  • Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0).
  • Pass a Comprehensive Exam.
  • Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester.
  • Complete all the steps for approval of their PhD dissertation.

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.

57 credit hours of 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).

Typical Schedule

Typically, a student’s first 3 years will follow a schedule like the one outlined below. The student’s remaining years will consist of electives and work on his or her research and dissertation.

  1. First year, fall: 2 required courses and 1 elective/research rotation course
    1. Intro to Data Science
    2. Probability and Statistics
    3. Elective/Research Rotation
  2. First year, spring: 2 required courses and 1 elective/research rotation course
    1. Big Data
    2. Machine Learning
    3. Elective/Research Rotation
  3. Second year, fall: 1 required courses and 2 electives with 1 being the research rotation course; identify research advisor
    1. Inference and Representation
    2. Elective/Research Rotation
    3. Approved Elective
  4. Second year, spring: 3 electives with 1 being the research rotation course; pass the Depth Qualifying Exam
    1. Elective/Research Rotation
    2. Approved Elective
    3. Approved Elective
  5. Third year, fall: 3 electives with 1 being the research rotation course
    1. Elective/Research Rotation
    2. Approved Elective
    3. Approved Elective
  6. Third year, spring: 3 electives with 1 being the research rotation course
    1. Elective/Research Rotation
    2. Approved Elective
    3. Approved Elective

Comprehensive Exam

The comprehensive exam is designed to determine whether the candidate displays the requisite data science knowledge in the areas of machine learning and big data. The exam consists of the final exams from the courses DS-GA 1003 Machine Learning and DS-GA 1004 Big Data.

The passing grade is A- or above. Students are expected to complete this requirement by the end of their second semester.

Students who do not pass the comprehensive exam will be placed on academic probation and must complete the requirement by the end of their fourth semester.

PhD students may sit for the final exam in these courses without registering for the courses.

Depth Qualifying Exam (DQE)

No later than the end of the third semester, each student must:

  • Agree on a research advisor. The student is responsible for finding a research advisor, obtaining an agreement to advise the student, and informing the Director of Graduate Studies (DGS) of the agreement. Students must reach agreement with the DGS and the Manager of Academic Affairs if they wish to change research advisors. If a research advisor determines that he or she no longer wishes to advise a student, the research advisor informs the DGS who will begin working with the student to find another research advisor.
  • Agree with his or her research advisor on a research project an exam topic, and a Depth Qualifying Exam (DQE) committee.
  • Obtain the approval of the DGS on the research project, exam topic, and DQE committee, as well as the date of the DQE exam.

No later than the end of his fourth semester, the student must pass the depth qualifying exam (DQE). The exam may be taken no more than twice. The content of the exam is defined by the student’s DQE Committee, which must present a syllabus to the student at least 2 months before the date of the exam.

The exam itself consists of two parts. The first part is a written or oral examination of the topics in the syllabus. The goal is to confirm the student’s knowledge of a research area that is distinct from the student’s own research area.The second part is a presentation by the student on original research carried out independently or in collaboration with faculty, research staff, or other students.

PhD Dissertation

Dissertation Proposal Approval

No later than May 15 of their third year, students must have their thesis proposal approved. The student works with their research advisor to select a dissertation proposal approval committee, obtains approval of this committee from the DGS, submits a written dissertation proposal to the committee, and obtains the approval of the committee. The committee consists of at least three members, which may consist of individuals with similar standing outside of CDS. At least one member must be a CDS Faculty member or CDS Affiliated Faculty member.

Dissertation Approval

Each student’s dissertation must be approved by all of the readers on the student’s defense committee. The PhD defense committee must have at least five members, including the advisor, three of whom must be core CDS faculty or affiliated faculty, and one external member (in related area from another NYU department or from an area institution, with approval from DGS). The membership of the defense committee is proposed by the student and approved by the DGS.

Approval of each reader is required. Their approvals are indicated by their signatures on a form provided by the Program Administrator or Manager of Academic Affairs. Their signatures are solicited by the student after the defense of her dissertation. The defense is a presentation and question-answering session in which the student presents her work. The NYU public is invited as are the members of the defense committee. The student works with the Program Administrator to arrange a date for the defense and to publicize the defense.

In addition, students must comply with all of the procedures of NYU’s Graduate of School of Arts and Science related to submission of their dissertation.