Â鶹´«Ã½Ó³»­

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 Â鶹´«Ã½Ó³»­ 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.