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

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

Master of Science in Data Science

The Master of Science in Data Science is designed for individuals with a Bachelor’s degree in a STEM related field who are looking to expand their knowledge and opportunities in Data Science. The program has a strong background in Data Mining, Modeling, Statistical and Machine learning.

Students will be prepared to use algorithms, statistics, and technology to make informed decisions from massive amounts of data, to manage streamed data or data stored in massive data warehouses, and to visually analyze and present information. Courses are designed to provide expertise in the data sciences and train students to solve problems with complex sets of structured and unstructured data commonly found in any industry. Students may either take a thesis track or non-thesis track.

Rowan University undergraduates majoring in the Bachelor of Science in Computer Science program may apply to the Accelerated Dual Degree (ADD) program which allows them to earn both the Bachelor of Science in Computer Science and the Master of Science in Data Science degrees in five years instead of six.

Required Courses: 9 s.h.

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 00500 - Computer Science Graduate Seminar

Credits: 0

CS 02505 - Data Mining I

Credits: 3

CS 07556 - Machine Learning I

Credits: 3

CS 07559 - Advanced Models of Deep Learning

Credits: 3

STAT 02515 - Applied Multivariate Data Analysis

Credits: 3

Core Courses: 6 s.h.

Choose two of the following courses:

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 02516 - Graduate Big Data Tools and Techniques

Credits: 3

CS 02620 - Data Warehousing

Credits: 3

DS 02510 - Visual Analytics

Credits: 3

ECE 09555 - Machine Learning

Credits: 3

MATH 01505 - Probability and Mathematical Statistics I

Credits: 3

MATH 03511 - Operations Research I

Credits: 3

STAT 02509 - Probability and Statistics for Data Science

Credits: 3

Elective Courses/Thesis: 15 s.h.

Thesis students must take 6 - 9 s.h. of Thesis Research courses.

Students may also use core courses as electives, if not counting for the Core Course requirements above.

Bank One

Select up to 5 courses from these data science offerings.

Course Name

Credits:

Term Taken

Grade

Gen Ed

BINF 05555 - Bioinformatics: Advanced Biological Applications

Credits: 3

CS 01541 - Bioinformatics - Advanced Computational Aspects

Credits: 3

CS 02530 - Advanced Database Systems: Theory and Programming

Credits: 3

CS 02570 - Information Visualization

Credits: 3

CS 02605 - Data Mining II

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 07558 - Large Language Models

Credits: 3

CS 07559 - Advanced Models of Deep Learning

Credits: 3

CS 07650 - Concepts in Artificial Intelligence

Credits: 3

CS 07656 - Machine Learning II

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

DS 01505 - Data Analytics Capstone Practicum

Credits: 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 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 09655 - Advanced Computational Intelligence and Machine Learning

Credits: 3

MATH 01506 - Probability and Mathematical Statistics II

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

Bank Two

Select no more than 2 courses form these data analytics offerings.

Course Name

Credits:

Term Taken

Grade

Gen Ed

CS 03552 - Digital Forensics

Credits: 3

DA 03510 - Patient Data Understanding

Credits: 3

DA 03511 - Patient Data Privacy and Ethics

Credits: 3

DHUM 52500 - Digital Humanities Debates and Methods

Credits: 3

GEOG 16560 - Digital Earth: Mapping and Geographic Information Science

Credits: 3

MGT 06603 - Process Analytics

Credits: 3

MGT 07510 - Quality Analytics

Credits: 3

MGT 07500 - Prescriptive Analytics

Credits: 3

MGT 07550 - Operations Analytics

Credits: 3

MGT 07600 - Predictive Analytics

Credits: 3

Thesis Courses

Thesis students should take Thesis I, Thesis II, and optionally Thesis III

Course Name

Credits:

Term Taken

Grade

Gen Ed

DS 03650 - Thesis I in Data Science

Credits: 3

DS 03651 - Thesis II in Data Science

Credits: 3

DS 03652 - Thesis III in Data Science

Credits: 3

Total Require Credits for the Program: 30 s.h.

Foundation Courses

Applicants must have successfully completed the following courses (or their equivalents) at an accredited institution: Calculus II, Probability & Statistical Inference for Computing Systems, Linear Algebra, Introduction to Object-Oriented Programming or Computer Science & Programming, and Data Structurces & Algorithms or Principles of Data Structures.

Graduation/Exit, Benchmark, and/or Thesis Requirements

A four (4) credit Capstone Practicum is required as part of the coursework.

Minimum Required Grades and Cumulative GPA

The Master of Science 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
breitzman@rowan.edu

Notes:

Course Name	Credits:	Term Taken	Grade	Gen Ed
Student ID:__________________________ Student Name:_______________________ Advisor Name:_______________________	Catalog: 2026-2027 Rowan University Academic Catalog Program: Master of Science in Data Science Minimum Credits Required:__________________
Master of Science in Data Science The Master of Science in Data Science is designed for individuals with a Bachelor’s degree in a STEM related field who are looking to expand their knowledge and opportunities in Data Science. The program has a strong background in Data Mining, Modeling, Statistical and Machine learning. Students will be prepared to use algorithms, statistics, and technology to make informed decisions from massive amounts of data, to manage streamed data or data stored in massive data warehouses, and to visually analyze and present information. Courses are designed to provide expertise in the data sciences and train students to solve problems with complex sets of structured and unstructured data commonly found in any industry. Students may either take a thesis track or non-thesis track. Rowan University undergraduates majoring in the Bachelor of Science in Computer Science program may apply to the Accelerated Dual Degree (ADD) program which allows them to earn both the Bachelor of Science in Computer Science and the Master of Science in Data Science degrees in five years instead of six.
Required Courses: 9 s.h.
CS 00500 - Computer Science Graduate Seminar	Credits: 0
CS 02505 - Data Mining I	Credits: 3

CS 07556 - Machine Learning I	Credits: 3
or
CS 07559 - Advanced Models of Deep Learning	Credits: 3

STAT 02515 - Applied Multivariate Data Analysis	Credits: 3
Core Courses: 6 s.h. Choose two of the following courses:
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 02516 - Graduate Big Data Tools and Techniques	Credits: 3
CS 02620 - Data Warehousing	Credits: 3
DS 02510 - Visual Analytics	Credits: 3
ECE 09555 - Machine Learning	Credits: 3
MATH 01505 - Probability and Mathematical Statistics I	Credits: 3
MATH 03511 - Operations Research I	Credits: 3
STAT 02509 - Probability and Statistics for Data Science	Credits: 3
Elective Courses/Thesis: 15 s.h. Thesis students must take 6 - 9 s.h. of Thesis Research courses. Students may also use core courses as electives, if not counting for the Core Course requirements above.
Bank One Select up to 5 courses from these data science offerings.
Course Name	Credits:	Term Taken	Grade	Gen Ed
BINF 05555 - Bioinformatics: Advanced Biological Applications	Credits: 3
CS 01541 - Bioinformatics - Advanced Computational Aspects	Credits: 3
CS 02530 - Advanced Database Systems: Theory and Programming	Credits: 3
CS 02570 - Information Visualization	Credits: 3
CS 02605 - Data Mining II	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 07558 - Large Language Models	Credits: 3
CS 07559 - Advanced Models of Deep Learning	Credits: 3
CS 07650 - Concepts in Artificial Intelligence	Credits: 3
CS 07656 - Machine Learning II	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
DS 01505 - Data Analytics Capstone Practicum	Credits: 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 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 09655 - Advanced Computational Intelligence and Machine Learning	Credits: 3
MATH 01506 - Probability and Mathematical Statistics II	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
Bank Two Select no more than 2 courses form these data analytics offerings.
Course Name	Credits:	Term Taken	Grade	Gen Ed
CS 03552 - Digital Forensics	Credits: 3
DA 03510 - Patient Data Understanding	Credits: 3
DA 03511 - Patient Data Privacy and Ethics	Credits: 3
DHUM 52500 - Digital Humanities Debates and Methods	Credits: 3
GEOG 16560 - Digital Earth: Mapping and Geographic Information Science	Credits: 3
MGT 06603 - Process Analytics	Credits: 3
MGT 07510 - Quality Analytics	Credits: 3
MGT 07500 - Prescriptive Analytics	Credits: 3
MGT 07550 - Operations Analytics	Credits: 3
MGT 07600 - Predictive Analytics	Credits: 3
Thesis Courses Thesis students should take Thesis I, Thesis II, and optionally Thesis III
Course Name	Credits:	Term Taken	Grade	Gen Ed
DS 03650 - Thesis I in Data Science	Credits: 3
DS 03651 - Thesis II in Data Science	Credits: 3
DS 03652 - Thesis III in Data Science	Credits: 3
Total Require Credits for the Program: 30 s.h. Foundation Courses Applicants must have successfully completed the following courses (or their equivalents) at an accredited institution: Calculus II, Probability & Statistical Inference for Computing Systems, Linear Algebra, Introduction to Object-Oriented Programming or Computer Science & Programming, and Data Structurces & Algorithms or Principles of Data Structures. Graduation/Exit, Benchmark, and/or Thesis Requirements A four (4) credit Capstone Practicum is required as part of the coursework. Minimum Required Grades and Cumulative GPA The Master of Science 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 breitzman@rowan.edu
Notes: