• Students who start the program with a recognized Master’s degree in Data Science or a related area, e.g., Computer Science, Information Science and Technology, etc., are required to take two 3-credit courses (6 credits) at the 600 level and four 3-credit courses (12 credits) at the 700 level.
  • Students who start the program with a recognized Baccalaureate degree are required to take eight 3-credit courses (24 credits) at either the 600 level or 700 level, as well as four additional 700-level 3-credit courses (12 credits), for a total of twelve 3-credit courses (36 credits).
  • All students must choose 18 credits of the required courses from sections designated as doctoral sections. A doctoral section is a section of a graduate-level course designated as such by the PhD committee. 700-level courses are always automatically considered doctoral sections.
  • At most 6 credits can be taken as Independent Study in Data Science (DS 725 and/or DS 726). If a student takes both Independent Study courses, then they should be taken with two different professors. At least 6 credits must be in lecture-based courses at the 700 level. Under rare circumstances, the student’s research advisor and dissertation committee might ask the student to take additional courses, usually before the Qualifying Exam, but possibly also after the Qualifying Exam.
  • Master’s project (course DS 700), Master’s thesis (course DS 701), or more than two independent study courses (courses DS 725 and DS 726) cannot be used to satisfy these coursework requirements.
  • Students will be required to take DS 675 (Machine Learning) or DS 644 (Introduction to Big Data), and also MATH 644 (Regression).
  • All required courses can be substituted by courses of equal difficulty, if the Ph.D. advisor and the Ph.D. directors in both tracks agree to them in writing. 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 academic advisor and dissertation advisor, a student is allowed to take elective courses based on the dissertation topic.

Core courses listed here are a selection. All courses may be found at https://catalog.njit.edu/   Required courses are highlighted.

DS 675 Machine Learning
DS 644 Introduction to Big Data
MATH 644 Regression Analysis Methods
DS 636 Data Analytics with R Programming
DS 677 Deep Learning
DS 642 Applications of Parallel Computing
DS 650 Data Visualization
MATH 660 Introduction to Statistical Computing
MATH 691 Stochastic Processes with Applications
MATH 699 Design and Analysis of Experiments

 

Core courses listed here are a selection. All courses may be found at https://catalog.njit.edu/ 

CS 610 Data Structures and Algorithms
CS 732 Advanced Machine Learning
CS 750 High Performance Computing
ECE 601 Linear Systems
ECE 673 Random Signal Analysis
IE 650  Advanced Topics in Operations Research
IE 687 Healthcare Enterprise Systems
MATH 787 Non-parametric statistics
MATH 786 Large Sample Theory and Inference