Master of Science in Data Science

Print Degree Planner (opens a new window) |

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 Advanced Dual Degree (4+1) 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.

Program Requirements

Required Courses: 7 s.h.

CS 00500 - Computer Science Graduate Seminar Credits: 1
CS 02505 - Data Mining I Credits: 3
STAT 02515 - Applied Multivariate Data Analysis Credits: 3

Core Courses: 9 s.h.

Choose three of the following courses:

CS 02516 - Big Data Tools and Techniques Credits: 3
CS 02620 - Data Warehousing Credits: 3
CS 07556 - Machine Learning I Credits: 3
DS 02510 - Visual Analytics Credits: 3
ECE 09555 - Machine Learning Credits: 3
ENGR 01511 - Engineering Optimization 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 and Thesis Courses: 15 s.h.

Choose 15 s.h. of the following courses (including thesis courses if a thesis student):

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
DA 03510 - Patient Data Understanding Credits: 3
DA 03511 - Patient Data Privacy & Ethics Credits: 3
DA 03520 - Healthcare Management Credits: 3
DHUM 52500 - Digital Humanities Debates & Methods Credits: 3
ECE 09585 - Advanced Engineering Cyber Security Credits: 3
ECE 09595 - Advanced Emerging Topics in Computational Intelligence, Machine Learning and Data Mining Credits: 1 to 3
ECE 09558 - Reinforcement Learning Credits: 3
ECE 09566 - Advanced Topics in Systems, Devices, and Algorithms in Bioinformatics Credits: 3
ECE 09568 - Discrete Event Systems Credits: 3
ECE 09655 - Advanced Computational Intelligence and Machine Learning Credits: 3
MATH 01506 - Probability and Mathematical Statistics II Credits: 3
MGT 07600 - Predictive Analytics 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
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: 31 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 and Programming, and Data Structurces and 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
Robinson Hall, Room 328P
breitzman@rowan.edu