M.S. in Computer Science — Agile

The M.S. in Computer Science — Agile curriculum builds rigorous, industry-ready computer scientists from the ground up, combining a strong theoretical foundation with hands-on mastery of the tools and paradigms defining modern software development. Students develop core proficiency in object-oriented programming, algorithms and data structures, and computer systems architecture, including GPU design, ARM and RISC-V processors, and virtualization, before advancing into theoretical computer science, advanced algorithms, and emerging programming paradigms. Electives in DevOps, mobile computing, human-computer interaction, and software system security give students the flexibility to tailor the degree to their goals, while AI-focused courses in machine learning, neural networks and deep learning, and artificial intelligence equip graduates to build and deploy the intelligent systems — from large language models and agentic workflows to autonomous vehicles and robotic systems — that are transforming every sector of the industry.

Equally forward-thinking is the program's integration of AI-augmented software engineering throughout the curriculum. Students learn to work alongside LLM-powered coding tools like Claude Code and Cursor to generate, refactor, and test code; automate CI/CD pipelines; and architect systems that incorporate AI-driven components and autonomous agents. The program's flexible Agile structure — completable full-time in two years or at a part-time pace — reflects the same adaptability it teaches, meeting students where they are while preparing them for where the field is going. A two-part capstone synthesizes it all into a production-quality deliverable, giving graduates a portfolio-ready project that demonstrates end-to-end engineering capability from design through deployment.

The 15-course (45-credit) Agile M.S. in Computer Science can be completed full time in 2 years, or part time at a pace that makes sense for you. View a typical full-time course sequence, review degree requirements, and download course descriptions below. 

Sample Full-Time Course Sequence

Course Descriptions

Degree Requirements

Core Course (5 courses / 15 credits)

• COM 5000 Introduction to Programming
• COM 5001 Computer Science Math I
• COM 5002 Algorithms and Data Structures
• COM 5003 Systems Analysis and Design
• COM 5010 Computer Systems

Advanced CS Core (3 courses / 9 credits)

• COM 5100 Advanced Algorithms
• COM 5101 Theoretical Computer Science and its Applications
• COM 5102 Emerging Paradigms in Programming

Electives (6 courses / 18 credits)*

• AIM 5006 Artificial Intelligence
• AIM 5001 Data Acquisition & Management
• AIM 5005 Machine Learning
• AIM 5007 Neural Network and Deep Learning
• AIM 5002 Computational Statistics and Deep Learning
• COM 5110 Operating Systems
• COM 5222 Fundamentals of Software Engineering
• COM 5323 Computer Graphics
• COM 5421 DevOps
• COM 5210 Mobile Computing and Apps Development
• COM 5120 Human-Computer Interaction
• COM 5440 Software System Security
• COM 5441 Hardware Security
• COM 5014 Special Topics (1-3 cr.)
• COM 5550 Internship (1-3 cr.)
• COM 5999 Independent Study (1-3 cr.)

Capstone (3 credits) 

• COM 6000 Capstone in Comp Sci 1 (1.5 cr.)
• COM 6001 Capstone in Comp Sci 2 (1.5 cr.)
   

*Electives: At least 12 credits must be from COM or AIM; additional elective courses may be selected from any graduate department at YU or elsewhere with permission of the program director. Offerings vary each semester. Therefore, some choices will not be available for a particular cohort. Internship can be taken as an elective beginning in the summer semester.

All courses are three credits unless otherwise noted.