نظرة عامة على البرنامج
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 List
| 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 |
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
Career Outlook & Job Opportunities
As a student in our Master of Science in Data Science program, you will strengthen your skills and better position yourself to pursue a variety of careers in the field of Data Science.
Admissions Information
Deadlines, Tuition and Financial Aid
Entry Terms & Deadlines Tuition 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.
Rates
We 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.
مخطط البرنامج
Degree Overview:
The Rowan University Master of Science (M.S.) in Data Science program prepares graduates with a STEM (Science, Technology, Engineering, or Math) background for careers in data science. The program focuses on data mining, modeling, and statistical and machine learning. Students can choose a concentration or select electives to specialize in computer science, statistics, or visual science. The program can be completed part-time or full-time.
Outline:
The M.S. in Data Science program comprises 11 courses totaling 31 graduate semester hours (s.h.). Applicants must have completed (or have equivalents of) 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 program is structured as follows:
- Required Courses (7 s.h.
- ):
- CS 00500 Computer Science Graduate Seminar (1 s.h.)
- STAT 02515 Applied Multivariate Data Analysis (3 s.h.)
- Core Courses (9 s.h., select three):
- CS 02516 Big Data Tools and Techniques (3 s.h.)
- CS 02620 Data Warehousing (3 s.h.)
- CS 07556 Machine Learning I (3 s.h.)
- DS 02510 Visual Analytics (3 s.h.)
- ECE 09555 Advanced Topics In Pattern Recognition (3 s.h.)
- ENGR 01511 Engineering Optimization (3 s.h.)
- MATH 01505 Probability and Mathematical Statistics I (3 s.h.)
- MATH 03511 Operations Research I (3 s.h.)
- STAT 02509 Probability and Statistics for Data Science (3 s.h.)
- Elective Courses/Thesis (15 s.h.
- ): Students choose from a wide range of electives listed under "Bank One" and "Bank Two," including courses in bioinformatics, advanced database systems, information visualization, data mining, data quality, algorithms, deep learning, artificial intelligence, and various statistics and data analytics courses.
Careers:
The M.S. in Data Science program aims to enhance students' skills and prepare them for various data science careers.
Other:
The program is offered in the Spring and Fall terms for international students. International students can defer their admission offers, with some restrictions for transfer students.
Entry Requirements:
For the M.S.
- Completed Application Form: A completed application form is required to initiate the application process.
- Completed Foundation Courses: Successful completion of 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.
- $65 (U.S.) Non-Refundable Application Fee: A non-refundable application fee of $65 USD is required.
- Bachelor's Degree: A bachelor's degree (or its equivalent) from an accredited institution of higher learning is necessary.
- Official Transcripts: Official transcripts from all colleges attended must be submitted. If documents are not in English, a certified English translation is required.
- Current Professional Resume: A current professional resume/CV is required.
- Statement of Professional Objectives: A typewritten statement outlining professional objectives is needed. Applicants should explain their reasons for pursuing the program, how they intend to use it to advance their careers, and any educational goals beyond the master's level. They should also highlight personal qualities like leadership or talent.
- Letters of Recommendation: Two letters of recommendation are required for M.S. applicants.
- Minimum Undergraduate GPA: A minimum undergraduate cumulative GPA of 2.5 (on a 4.0 scale) is required. The text also mentions that if scores are too low, conditional admission may be offered, allowing students to participate in the English Language Program to improve their skills before entering the university. Therefore, the specific requirements must be obtained from Rowan's English Language Policy.
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