Degree requirements for the PhD in Data Science can be found at http://gsas.nyu.edu/bulletin/data-science/phd-data-science.html.
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.
- DS-GA 1001 – Introduction to Data Science
- DS- GA1002 – Probability and Statistics for Data Science
- DS-GA 1003 – Machine Learning and Computational Statistics
- DS-GA 1004 – Big Data
- DS-GA 1005 – Inference and Representation
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).
Typically, a student will follow a schedule like the one outlined here:
- First year, fall: 2 required courses and 1 elective/research rotation courses
- Intro to Data Science
- Probability and Statistics
- Pre-approved Elective
- First year, spring: 2 required courses and 1 elective/research rotation course
- Big Data
- Machine Learning and Computational Statistics
- Pre-approved Elective
- Second year, fall: 1 required courses and 2 electives/research rotation courses
- Inference and Representation
- Second year, spring: 3 electives/research rotation courses, pass the Depth Qualifying 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 Computational Statistics and DS-GA 1004 Big Data. The passing grade is A.
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 Program Administrator if they wish to change research advisors. If a research advisor determines that she no longer wishes to advise a student, the research advisors informs the DGS who will begin working with the student to find another research advisor.
- Agree with his 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.
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 thesis approval committee, obtains approval of this committee from the DGS, submits a written thesis 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.
Each student’s dissertation must be approved by all of the readers on the student’s defense committee. The PhD committee must have at least four members, including the advisor, three of whom must be core CDS faculty or affiliated faculty. 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. 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.