Ph.D. in Data Science: Statistics Option

PhD students with a recognized Baccalaureate degree are required to take ten (NJIT minimum: eight) 600-level or 700-level 3-credit courses (30 credits) of coursework beyond the Baccalaureate degree as well as four additional 700-level 3-credit courses (12 credits), for a total of fourteen (NJIT minimum: twelve) 3-credit courses (42 credits).

 

PhD students with a recognized Master’s degree or equivalent are required to take seven graduate courses, of which four should be 700-level 3-credit courses (21 credits). (NJIT minimum: four 700-level courses.)

 

Master’s project (course MATH 700), Master’s thesis (course MATH 701), or more than two independent study courses (courses MATH 725 and MATH 726) cannot be used to satisfy these coursework requirements.

 

Students will be required to take DS 675 (Machine Learning), MATH 644 (Regression), and MATH 631 (Linear Algebra).

 

All required courses can be substituted by courses of equal difficulty, if the PhD advisor and the PhD directors in both options agree to them in writing (e-mail is sufficient). For example, if a student has already taken an equivalent course to a required course, then a substitute will be determined.

 

In addition, the Statistics Track requires a two-part qualifying exam while the Computing Track requires a single-part qualifying exam. Both tracks require a proposal document, a proposal defense, a dissertation document, and a dissertation defense. Publication requirements will be defined by the track directors. Students may request to transfer between the tracks prior to passing their qualifying exams.

 

With approval by the Ph.D. program Director and dissertation advisor, a student is allowed to take elective courses based on the dissertation topic.