M.S. in Data Science (M.S. in D.S.)
Program Overview
The Rowan Experience
Our Master of Science (M.S.) in Data Science prepares graduates with a degree in a Science, Technology, Engineering or Math (STEM) related field for a career in data science. The program provides a strong background in data mining, modeling, and statistical and machine learning. In our curriculum, we build on industry needs, as well as guidelines of the Commission on Accreditation for Health Informatics and Information Management Education (C.A.H.I.I.M.) and the Technology Accreditation Commission of the Accreditation Board for Engineering and Technology (A.B.E.T.).
As a student, you may declare a concentration or choose from a variety of electives to increase your knowledge of computer science, statistics or visual science.
Curriculum
The Master of Science (M.S.) in Data Science program consists of 11 courses and a total of 31 graduate semester hours (s.h.). Students may enroll in this program part-time or full-time.
Applicants must have successfully completed the following courses (or their equivalents) at an accredited institution: Calculus II, Probability and Statistical Inference for Computing Systems, Linear Algebra, Introduction to Object-Oriented Programming or Computer Science and Programming, and Data Structures and Algorithms or Data Structures for Engineers.
The following courses make up the M.S. in Data Science program.
- 11 Courses/ 31 Semester Hours
- Foundation Courses: Yes
- Graduation / Exit / Thesis Requirements: No
| Course Number | Title | S.H. (Credits) |
|---|---|---|
| Required Courses: 7 S.H. | ||
| CS 00500 | Computer Science Graduate Seminar | 1 |
| CS 02505 | Data Mining I | 3 |
| STAT 02515 | Applied Multivariate Data Analysis | 3 |
| Core Courses: 9 S.H. (select three courses) | ||
| CS 02516 | Big Data Tools and Techniques | 3 |
| CS 02620 | Data Warehousing | 3 |
| CS 07556 | Machine Learning I | 3 |
| DS 02510 | Visual Analytics | 3 |
| ECE 09555 | Advanced Topics In Pattern Recognition | 3 |
| ENGR 01511 | Engineering Optimization | 3 |
| MATH 01505 | Probability and Mathematical Statistics I | 3 |
| MATH 03511 | Operations Research I | 3 |
| STAT 02509 | Probability and Statistics for Data Science | 3 |
| Elective Courses/Thesis: 15 S.H. | ||
| Bank One (select up to 5 courses from these data science offerings) | ||
| BINF 05555 | Bioinformatics - Advanced Biological Applications | 3 |
| CS 01541 | Bioinformatics - Advanced Computational Aspects | 3 |
| CS 02530 | Advanced Database Systems: Theory and Programming | 3 |
| CS 02570 | Information Visualization | 3 |
| CS 02605 | Data Mining II | 3 |
| CS 02625 | Data Quality and Web/Text Mining | 3 |
| CS 02630 | Advanced Topics in Database Systems | 3 |
| CS 07540 | Advanced Design and Analysis of Algorithms | 3 |
| CS 07559 | Advanced Models of Deep Learning | 3 |
| CS 07650 | Concepts in Artificial Intelligence | 3 |
| CS 07656 | Machine Learning II | 3 |
| DS 01505 | Data Science Capstone Practicum | 3 |
| DA 03510 | Patient Data Understanding | 3 |
| DA 03511 | Patient Data Privacy & Ethics | 3 |
| DA 03520 | Healthcare Management | 3 |
| DHUM 52500 | Digital Humanities Debates & Methods | 3 |
| ECE 09558 | Reinforcement Learning | 3 |
| ECE 09560 | Artificial Neural Networks | 3 |
| ECE 09566 | Advanced Topics in Systems, Devices, and Algorithms in Bioinformatics | 3 |
| ECE 09568 | Discrete Event Systems | 3 |
| ECE 09585 | Advanced Engineering Cyber Security | 3 |
| ECE 09586 | Advanced Portable Platform Development | 3 |
| ECE 09595 | Advanced Emerging Topics in Computational Intelligence, Machine Learning, & Data Mining | 3 |
| ECE 09655 | Advanced Computational Intelligence and Machine Learning | 3 |
| MATH 01506 | Probability and Mathematical Statistics II | 3 |
| STAT 02510 | Introduction to Statistical Data Analysis | 3 |
| STAT 02514 | Decision Analysis | 3 |
| STAT 02525 | Design and Analysis of Experiments | 3 |
| STAT 02530 | Applied Survival Analysis | 3 |
| STAT 02585 | Introduction to Bayesian Statistical Methods | 3 |
| Bank Two (select no more than 2 courses from these data analytics offerings) | ||
| CS 03552 | Graduate Digital Forensics | 3 |
| DHUM 52500 | Digital Humanities Debates & Methods | 3 |
| GEOG 16560 | Digital Earth: Mapping & Geographic Information Science | 3 |
| MGT 06603 | Process Analytics | 3 |
| MGT 07500 | Prospective Analytics | 3 |
| MGT 07510 | Quality Analytics | 3 |
| MGT 07550 | Operations Analytics | 3 |
| MGT 07600 | Predictive Analytics | 3 |
| Thesis students should take Thesis I, Thesis II, and optionally Thesis III | ||
| DS 03650 | Thesis in Data Science I | 3 |
| DS 03651 | Thesis in Data Science II | 3 |
| DS 03652 | Thesis in Data Science III | 3 |
Note: The courses listed above are not official and are subject to change. For an official list of available courses please visit the Rowan Global section tally.
Admission Requirements
The following is a list of items required to begin the application process for the program. There may be additional actions or materials required for admission to the program. Upon receipt of the materials below, a representative from the Rowan Global Admissions Processing Office will contact you with confirmation or will indicate any missing items.
- Completed Application Form
- Completed foundation courses
- $65 (U.S.) non-refundable application fee
- Bachelor's degree (or its equivalent) from an accredited institution of higher learning
- Official transcripts from all colleges attended (regardless of number of credits earned)
- Current professional resume
- Typewritten statement of professional objectives
- Provide reasons for pursuing the program. Describe how you might use this program to advance your career (educational goals beyond the master's level, if applicable, are also relevant)
- Two letters of recommendation
- Minimum undergraduate cumulative GPA of 2.5 (on a 4.0 scale)
- Submission of official GRE test results is highly recommended
Admissions Information
Deadlines, Tuition and Financial Aid
The chart below details available entry terms for the M.S. in Data Science program as well as corresponding application deadlines. Submitting the Application Form is only the first step to beginning the admission process. All of the required materials listed above must be received on or before the application completion deadline for your desired entry term to be considered for admission to that term. We encourage you to complete the application form and begin submitting your materials at least one month before the deadline indicated.
| Entry Term | Application Deadline |
|---|---|
| Fall | July 1 |
| Spring | November 1 |
At Rowan University, we pride ourselves on being vigilant and frugal about tuition. We work hard to provide quality education while seeking to reduce the barrier that college costs can present students.
RatesWe know paying for tuition can be a challenge. That is why Rowan provides students with the financial resources needed to put their education first by offering grants, loans, work-study, and scholarships.
More InfoThe chart below details available entry terms for the M.S. in Data Science program as well as corresponding application deadlines. Submitting the Application Form is only the first step to beginning the admission process. All of the required materials listed above must be received on or before the application completion deadline for your desired entry term to be considered for admission to that term. We encourage you to complete the application form and begin submitting your materials at least one month before the deadline indicated.
| Entry Term | Application Deadline |
|---|---|
| Fall | July 1 |
| Spring | November 1 |
At Rowan University, we pride ourselves on being vigilant and frugal about tuition. We work hard to provide quality education while seeking to reduce the barrier that college costs can present students.
RatesWe know paying for tuition can be a challenge. That is why Rowan provides students with the financial resources needed to put their education first by offering grants, loans, work-study, and scholarships.
More InfoLearn More
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