Curriculum for Advanced Professional Practice
The Master of Computer Science curriculum is designed to ensure you graduate with the expertise and experience to excel in your chosen industry or specialization.
Core subjects (taken by all students) are combined with flexible electives grouped into recommended technical pathways – allowing you to align your subject choices with your career goals.
Core curriculum
All MCS students will learn advanced programming, algorithmic problem solving, project development, communication, and applied experience by completing the following core subjects:
- COMPSCI 253P: Advanced Programming and Problem Solving
- COMPSCI 260P: Algorithms with Applications
- COMPSCI 261P: Data Structures with Applications
- COMPSCI 295P: Keystone Project for Computer Science
- COMPSCI 296P: Capstone Professional Writing and Communication for Computer Science Careers
- COMPSCI 297P: Capstone Design Project for Computer Science
- COMPSCI 298P: Computer Science Practicum.
Elective curriculum
Build depth of expertise in AI, data engineering or software systems through recommended pathways that group electives according to common professional goals, or select your own elective subjects according to your specific career goals and preferences.
Recommended elective pathways
- Intelligent Systems & AI: Topics covered include artificial intelligence, machine learning, deep learning, search, language technologies, and intelligent information systems.
- Data Engineering & Analytics: Topics covered include databases, large-scale data systems, distributed data management, analytics, and data-intensive application design.
- Computing Systems & Infrastructure: Topics covered include systems-level software, computer architecture, distributed computing, performance, and scalable computing platforms.
Additional elective subjects: Security
Computer and network security are additional elective subjects that are relevant across multiple modern computing domains. Our COMPSCI 201P: Computer Security and COMPSCI 203P: Network Security courses complement work in AI, data-intensive computing, and software/systems.
Experiential learning: Capstone project
The program culminates in a capstone project where you will collaborate with fellow students, faculty, and industry partners to develop and demonstrate solutions for a real-world problem.
For more information on individual courses and program requirements, please visit UC Irvine’s Master of Computer Science course catalogue.
Sample course timetable with recommended pathways
| Quarter | Core Subject | Intelligent Systems & AI | Data Engineering & Analytics | Software Systems & Infrastructure |
|---|---|---|---|---|
| Fall (Year 1) | 253P Advanced Programming and Problem Solving | 271P Artificial Intelligence | 220P Databases and Data Management | 238P Operating Systems |
| 260P Algorithms with Applications | ||||
| Winter (Year 1) | 295P Keystone Project for Computer Science | 273P Machine Learning and Data Mining | 223P Transaction Processing and Distributed Data Management | 250P Computer Systems Architecture |
| 261P Data Structures with Applications | ||||
| Spring (Year 1) | 296P Capstone Professional Writing and Communication for Computer Science Careers | 274P Neural Networks and Deep Learning | 222P Principles of Data Management | 231P Parallel and Distributed Computing for Professionals |
| 297P Capstone Design Project for Computer Science | ||||
| Summer | 298P Computer Science Practicum | |||
| Fall (Year 2) | - | 262P Text Processing and Information Retrieval | 224P Big Data Management | 244P Internet of Things |
| 201P Computer Security | 201P Computer Security | 201P Computer Security | ||
| 203P Network Security | 203P Network Security | 203P Network Security |