Ph.D. in Data Science: Statistics Option

Students wishing to pursue the Statistics option of the Ph.D. in Data Science will meet the following requirements.

Department of Data Science
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.
Content
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
MATH 631 Linear Algebra
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
Content
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  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
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