Program Overview

Program Overview

The Computer Science program at Rutgers-Camden offers a Master of Science (M.S.) degree with a concentration in scientific computing, preparing students for data-intensive careers in science, engineering, or finance. The program provides a strong foundation in algorithms and programming tailored to current and emerging computational applications.

Program Details

• Degree: Master of Science (M.S.)
• 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)

Admissions Requirements

• 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

• Fall: March 15
• Spring: October 15
• Summer: Not offered

Featured Courses

• Machine Learning (56:198:554): A comprehensive introduction to machine learning and data mining, covering theory, algorithms, and practical applications in various domains.
• Applied Probability (56:198:567): Introduction to probability theory, emphasizing computer science, engineering, and data science applications.
• Artificial Intelligence (56:198:514): Introduction to AI concepts, including intelligent agents, heuristic approaches, logic inference, knowledge representation, probabilistic reasoning, and Bayesian belief networks.
• Network Security (50:198:547): In-depth training in network security, covering topics like network design, access control, firewalls, intrusion prevention, VPNs, and more.
• Software Engineering (50:198:523): Exploration of 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): Exploration of algorithms and modeling for analyzing massive data, including information retrieval, streaming algorithms, and web search analysis.

Research Areas

The program 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. 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 Community

The program is part of a vibrant community that includes the Graduate Student Advisory Council and the Graduate Student Organization (GSO), offering opportunities for student activity, on-campus living, safety and security, transportation services, on-campus jobs, and events. The university is committed to diversity and inclusion, providing a supportive environment for all students.