Student ID:__________________________ Student Name:_______________________ Advisor Name:_______________________	Catalog: 2026-2027 Rowan University Academic Catalog Program: Doctor of Philosophy in Data Science Minimum Credits Required:__________________
Doctor of Philosophy in Data Science The Ph.D. in Data Science program will provide the essential skills required to analyze big and complex data sets and equip students with a broad understanding of data challenges and opportunities, along with the research and inquiry skills necessary to independently conduct research and answer questions within their area of concentration. To meet this goal, courses in the Ph.D. in Data Science Program curriculum are organized around interdisciplinary focal areas in computer science, engineering, mathematics, and statistics. Courses offered within this framework include traditional lecture-style, e-learning, and special topics courses that introduce students to the latest theories, methods, and emerging issues; seminar series; and experiential learning. Through this framework, students will gain proficiency in the application of scientific principles such as, critical thinking, experimental design, data preprocessing and wrangling, data visualization, advanced statistical learning/data mining and machine learning, as well as a sense of professional and technical writing, and reporting, responsibility, and integrity. Students possessing a bachelor’s degree will be required to complete a minimum of 72 semester hours of graduate-level work. Students possessing a master’s degree in a related field will be required to complete a minimum of 42 semester hours of graduate-level work beyond their master’s degree in addition to meeting other Ph.D. requirements in the section below. Up to 30 of the credits earned in pursuit of your master’s degree may be transferable to the Ph.D. program as either core courses or elective courses.
Required Courses: 15 s.h. Choose five of the following courses:
Data Mining & Visualization
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 02516 - Graduate Big Data Tools and Techniques	Credits: 3
Probability & Statistics
Course Name	Credits:	Term Taken	Grade	Gen Ed
MATH 01505 - Probability and Mathematical Statistics I	Credits: 3
STAT 02515 - Applied Multivariate Data Analysis	Credits: 3
Machine Learning (take one of the following)
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 07556 - Machine Learning I	Credits: 3
ECE 09555 - Machine Learning	Credits: 3
Decision Optimization (take one of the following)
Course Name	Credits:	Term Taken	Grade	Gen Ed
MATH 03511 - Operations Research I	Credits: 3
General Courses: 6 s.h.
Course Name	Credits:	Term Taken	Grade	Gen Ed
ECE 09702 - Strategic Technical Writing and Winning Grant Proposals	Credits: 3
XEED 01601 - Effective Teaching in Academic, Corporate and Government Settings	Credits: 3
Elective Courses: 21-30 s.h. A minimum of 21 and a maximum of 30 semester hours of elective coursework are required. Courses will be recommended by a student’s thesis advisor to align with their research area. Elective courses and thesis research must total 51 semester hours. The distribution between these two areas will be determined by the student and their thesis advisor. Choose between seven and ten of the following courses:
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 02505 - Data Mining I	Credits: 3
CS 02530 - Advanced Database Systems: Theory and Programming	Credits: 3
CS 02605 - Data Mining II	Credits: 3
CS 02620 - Data Warehousing	Credits: 3
CS 02625 - Data Quality and Web/Text Mining	Credits: 3
CS 02630 - Advanced Topics in Database Systems	Credits: 3
CS 07540 - Advanced Design and Analysis of Algorithms	Credits: 3
CS 07559 - Advanced Models of Deep Learning	Credits: 3
CS 07558 - Large Language Models	Credits: 3
CS 07656 - Machine Learning II	Credits: 3
DS 02510 - Visual Analytics	Credits: 3
DS 02695 - Advanced Topics in Data Science (Only the 3 s.h. version of these courses can count as a Restricted Elective)	Credits: 1 to 4
ECE 09595 - Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining (Only the 3 s.h. version of these courses can count as a Restricted Elective)	Credits: 1 to 3
ECE 09558 - Reinforcement Learning	Credits: 3
ECE 09560 - Artificial Neural Networks	Credits: 3
ECE 09566 - Advanced Topics in Systems, Devices, and Algorithms in Bioinformatics	Credits: 3
ECE 09568 - Discrete Event Systems	Credits: 3
ECE 09585 - Advanced Engineering Cyber Security	Credits: 3
ECE 09586 - Advanced Portable Platform Development	Credits: 3
ECE 09655 - Advanced Computational Intelligence and Machine Learning	Credits: 3
MATH 01506 - Probability and Mathematical Statistics II	Credits: 3
STAT 02509 - Probability and Statistics for Data Science	Credits: 3
STAT 02510 - Introduction to Statistical Data Analysis	Credits: 3
STAT 02511 - Statistical Computing	Credits: 3
STAT 02514 - Decision Analysis	Credits: 3
STAT 02525 - Design and Analysis of Experiments	Credits: 3
STAT 02530 - Applied Survival Analysis	Credits: 3
STAT 02585 - Introduction to Bayesian Statistical Methods	Credits: 3
Thesis Research: 21-30 s.h.
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 02799 - Doctoral Research and Dissertation	Credits: 1 to 9
INTR 01700 - PhD Dissertation Research Continuation *	Credits: 9
* to optionally be used, as needed, with approval of advisor.
Degree Completion Requirements For students possessing a master’s degree in a related field, degree completion requirements are a minimum of 42 s.h. beyond their master’s degree. Additionally, these students must complete all core courses that have not transferred. If 30 s.h. hours are not transferred into the Ph.D. program, students will be required to take additional courses such that the total of transferred credits and credits earned at Rowan University total 72 s.h.
Total Required Credits for the Program: 72 s.h. Foundation Courses None Graduation/Exit, Benchmark, and/or Thesis Requirements A minimum of 21 and a maximum of 30 semester hours of thesis research are required. Thesis research and elective courses must total 51 semester hours. The distribution between these two areas will be determined by the student and their thesis advisor. Students must successfully complete and defend Dissertation. Minimum Required Grades and Cumulative GPA The Doctor of Philosophy in Data Science is a Category 3 program. For details regarding satisfactory academic progress and graduation requirements, please visit Academic Program Policy Categories Program Coordinator/Advisor Contact Information Anthony Breitzman Robinson Hall 328P breitzman@rowan.edu
Notes:

Student ID:__________________________ Student Name:_______________________ Advisor Name:_______________________

Catalog: 2026-2027 Rowan University Academic Catalog Program: Doctor of Philosophy in Data Science Minimum Credits Required:__________________

Doctor of Philosophy in Data Science

The Ph.D. in Data Science program will provide the essential skills required to analyze big and complex data sets and equip students with a broad understanding of data challenges and opportunities, along with the research and inquiry skills necessary to independently conduct research and answer questions within their area of concentration.

To meet this goal, courses in the Ph.D. in Data Science Program curriculum are organized around interdisciplinary focal areas in computer science, engineering, mathematics, and statistics. Courses offered within this framework include traditional lecture-style, e-learning, and special topics courses that introduce students to the latest theories, methods, and emerging issues; seminar series; and experiential learning. Through this framework, students will gain proficiency in the application of scientific principles such as, critical thinking, experimental design, data preprocessing and wrangling, data visualization, advanced statistical learning/data mining and machine learning, as well as a sense of professional and technical writing, and reporting, responsibility, and integrity.

Students possessing a bachelor’s degree will be required to complete a minimum of 72 semester hours of graduate-level work. Students possessing a master’s degree in a related field will be required to complete a minimum of 42 semester hours of graduate-level work beyond their master’s degree in addition to meeting other Ph.D. requirements in the section below. Up to 30 of the credits earned in pursuit of your master’s degree may be transferable to the Ph.D. program as either core courses or elective courses.

Required Courses: 15 s.h.

Choose five of the following courses:

Data Mining & Visualization

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 02516 - Graduate Big Data Tools and Techniques

Credits: 3

Probability & Statistics

Course Name

Credits:

Term Taken

Grade

Gen Ed

MATH 01505 - Probability and Mathematical Statistics I

Credits: 3

STAT 02515 - Applied Multivariate Data Analysis

Credits: 3

Machine Learning

(take one of the following)

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 07556 - Machine Learning I

Credits: 3

ECE 09555 - Machine Learning

Credits: 3

Decision Optimization

(take one of the following)

Course Name

Credits:

Term Taken

Grade

Gen Ed

MATH 03511 - Operations Research I

Credits: 3

General Courses: 6 s.h.

Course Name

Credits:

Term Taken

Grade

Gen Ed

ECE 09702 - Strategic Technical Writing and Winning Grant Proposals

Credits: 3

XEED 01601 - Effective Teaching in Academic, Corporate and Government Settings

Credits: 3

Elective Courses: 21-30 s.h.

A minimum of 21 and a maximum of 30 semester hours of elective coursework are required. Courses will be recommended by a student’s thesis advisor to align with their research area. Elective courses and thesis research must total 51 semester hours. The distribution between these two areas will be determined by the student and their thesis advisor.

Choose between seven and ten of the following courses:

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 02505 - Data Mining I

Credits: 3

CS 02530 - Advanced Database Systems: Theory and Programming

Credits: 3

CS 02605 - Data Mining II

Credits: 3

CS 02620 - Data Warehousing

Credits: 3

CS 02625 - Data Quality and Web/Text Mining

Credits: 3

CS 02630 - Advanced Topics in Database Systems

Credits: 3

CS 07540 - Advanced Design and Analysis of Algorithms

Credits: 3

CS 07559 - Advanced Models of Deep Learning

Credits: 3

CS 07558 - Large Language Models

Credits: 3

CS 07656 - Machine Learning II

Credits: 3

DS 02510 - Visual Analytics

Credits: 3

DS 02695 - Advanced Topics in Data Science (Only the 3 s.h. version of these courses can count as a Restricted Elective)

Credits: 1 to 4

ECE 09595 - Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining (Only the 3 s.h. version of these courses can count as a Restricted Elective)

Credits: 1 to 3

ECE 09558 - Reinforcement Learning

Credits: 3

ECE 09560 - Artificial Neural Networks

Credits: 3

ECE 09566 - Advanced Topics in Systems, Devices, and Algorithms in Bioinformatics

Credits: 3

ECE 09568 - Discrete Event Systems

Credits: 3

ECE 09585 - Advanced Engineering Cyber Security

Credits: 3

ECE 09586 - Advanced Portable Platform Development

Credits: 3

ECE 09655 - Advanced Computational Intelligence and Machine Learning

Credits: 3

MATH 01506 - Probability and Mathematical Statistics II

Credits: 3

STAT 02509 - Probability and Statistics for Data Science

Credits: 3

STAT 02510 - Introduction to Statistical Data Analysis

Credits: 3

STAT 02511 - Statistical Computing

Credits: 3

STAT 02514 - Decision Analysis

Credits: 3

STAT 02525 - Design and Analysis of Experiments

Credits: 3

STAT 02530 - Applied Survival Analysis

Credits: 3

STAT 02585 - Introduction to Bayesian Statistical Methods

Credits: 3

Thesis Research: 21-30 s.h.

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 02799 - Doctoral Research and Dissertation

Credits: 1 to 9

INTR 01700 - PhD Dissertation Research Continuation *

Credits: 9

* to optionally be used, as needed, with approval of advisor.

Degree Completion Requirements

For students possessing a master’s degree in a related field, degree completion requirements are a minimum of 42 s.h. beyond their master’s degree. Additionally, these students must complete all core courses that have not transferred. If 30 s.h. hours are not transferred into the Ph.D. program, students will be required to take additional courses such that the total of transferred credits and credits earned at Rowan University total 72 s.h.

Total Required Credits for the Program: 72 s.h.

Foundation Courses

None

Graduation/Exit, Benchmark, and/or Thesis Requirements

A minimum of 21 and a maximum of 30 semester hours of thesis research are required. Thesis research and elective courses must total 51 semester hours. The distribution between these two areas will be determined by the student and their thesis advisor. Students must successfully complete and defend Dissertation.

Minimum Required Grades and Cumulative GPA

The Doctor of Philosophy in Data Science is a Category 3 program.

For details regarding satisfactory academic progress and graduation requirements, please visit Academic Program Policy Categories

Program Coordinator/Advisor Contact Information
Anthony Breitzman
Robinson Hall 328P
breitzman@rowan.edu

Notes: