Computer Science (M.S.)
Welcome to the Computer Science program at Rutgers-Camden. Our master’s degree program, with a concentration in scientific computing, prepares students for data-intensive careers in science, engineering, or finance. The program offers a strong foundation in algorithms and programming tailored to current and emerging computational applications.
TAKE THE NEXT STEP
The Computer Science program at Rutgers-Camden equips graduates with a solid foundation in theory and algorithms, enabling them to pursue further educational opportunities in Ph.D. programs in science and engineering at leading academic institutions in the U.S. and internationally. With a focus on relevant algorithms and programming, students also acquire the necessary skills for current and emerging computational applications in the industry.
Through rigorous coursework and the option for a project or thesis, students develop both theoretical knowledge and practical abilities to address diverse computational challenges, including:
Big-data analytics
Modeling proteins for drug discovery
Mining massive internet transaction datasets
Forecasting ecosystem behavior
Program Overview
DETAIL | DESCRIPTION |
|---|---|
| DEGREE | Master of Science (M.S.) Accelerated option available |
| CREDITS | 30 credits |
| FORMAT | Full-time or part-time, on-campus |
| DURATION | 4-5 semesters |
| FUNDING | University and Graduate School Funding Available (Partial Funding Only) |
Computer Science Community in Action
Get to know this program through archived stories—then discover what sets Rutgers Graduate School-Camden apart. From the Recording Rutgers podcast to faculty spotlights, campus life, alumni journeys, and student experiences, you’ll find a “Community in Action”, grounded in connection, purpose, and possibility.
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Using Deep Learning to Predict Brain Aging from MRI Scans
Eshaa Gogia, an alum of the Data Science MS program at Rutgers–Camden, and Iman Dehzangi, assistant professor in the Department of Computer Science, coauthored a new study with Arash Dehzangi. Their article, “Automated Subregional Hippocampus Segmentation Using 3D CNNs: A Computational Framework for Brain Aging Biomarker Analysis,” was published in Algorithms as part of a… continue reading
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Reimagining Agriculture through Robots & AI
When the American Cranberry Growers Association gathered for its annual… continue reading
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Strong Finish to a GSO-Powered Year
Even gray skies couldn’t dim the spirit of the Graduate… continue reading
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Diwali Lights Up Rutgers-Camden
Being away from home during festive times can be challenging… continue reading
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Data to Dreams: Enhancing Sleep Research
Sleep disruptions are closely linked to neurodegenerative diseases, underscoring the… continue reading
Featured Courses
View the list of Course Descriptions.
MACHINE LEARNING
(56:198:554)
This course provides a comprehensive introduction to machine learning and data mining, covering theory, algorithms, and practical applications in various domains.
APPLIED PROBABILITY
(56:198:567)
This course introduces probability theory, emphasizing computer science, engineering, and data science applications. Topics include probabilistic models, random variables, Markov chains, and statistical inference.
ARTIFICIAL INTELLIGENCE (56:198:514)
This course introduces AI concepts, including intelligent agents, heuristic approaches, logic inference, knowledge representation, probabilistic reasoning, and Bayesian belief networks.
NETWORK SECURITY
(50:198:547)
This course provides in-depth training in network security, covering topics like network design, access control, firewalls, intrusion prevention, VPNs, and more.
SOFTWARE ENGINEERING (50:198:523)
This course explores principles for designing reliable, maintainable software systems, covering topics like the software lifecycle, requirements, validation, implementation, and user interfaces.
BIG DATA ALGORITHMS (56:198:562)
This course explores algorithms and modeling for analyzing massive data. Topics include information retrieval, streaming algorithms, and web search analysis.
Admissions Requirements
DETAIL | DESCRIPTION |
|---|---|
| TRANSCRIPTS | Official transcripts showing a bachelor’s degree with a minimum GPA of 3.0 and a firm foundation in linear algebra and multivariable calculus. A bachelor’s degree in a basic science or engineering field is preferred but not required. |
| LETTERS OF RECOMMENDATION | At least two letters of recommendation, preferably from academic references, should be presented on official letterhead and include the referees’ contact information |
| PERSONAL STATEMENT | Personal statement (maximum two pages) about academic interests and career goals |
| STANDARDIZED TEST | GRE scores preferred but not required |
Application Deadlines
Preference is given to those submitted before the deadline for both decisions and funding opportunities. Applications received after the deadlines will be considered based on available space and budgetary constraints.
Fall
MARCH 15
Spring
OCT 15
Summer
NOT OFFERED
Reach Out to Us
Let’s begin a conversation about your academic and professional goals. If you have questions regarding the curriculum, faculty, admission criteria, or committee, please feel free to contact the Graduate Program Director. For administrative inquiries concerning the application process, campus tuition, and fees, reach out to the Graduate School. We are dedicated to assisting you from application to graduation day.
GRADUATE SCHOOL CONTACT
Erick E. Watt-Udogu
Assistant Dean, Graduate School-Camden
erick.watt.udogu@camden.rutgers.edu
(856) 225-6149
Take the Next Step.
We invite you to explore the boundless opportunities that await you here, where a world-class faculty, diverse community, and innovative curriculum converge to nurture your intellect and foster your professional growth. Your future starts with a simple yet significant act – learning more about the exceptional programs we offer. So, why wait? Start your application journey with us, and together, we’ll unlock a future filled with possibilities and endless success.