Self-Directed Master's in Computer Science Curriculum

Course Structure

Duration: 18-24 months (part-time, evenings/weekends)
Format: Concept-focused with practical implementation
Background: Assumes 20 years software development experience
Goal: Fill theoretical gaps and provide CS academic foundation

Core Requirements (Months 1-12)

All MS students take these regardless of specialization

1. Algorithms and Complexity Analysis (Months 1-2)

Goal: Understand algorithmic thinking and complexity analysis beyond day-to-day programming

Topics:

• Big-O analysis and recurrence relations
• Divide and conquer algorithms
• Dynamic programming
• Greedy algorithms
• Graph algorithms (DFS, BFS, shortest paths, MST)
• Basic complexity classes (P, NP, NP-complete)
• Algorithm design strategies

Practical Focus:

• Analyze complexity of code you write daily
• Implement classic algorithms from scratch
• Solve competitive programming problems

Key Resource: Algorithm Design by Kleinberg and Tardos
Project: Implement a graph algorithm library with performance analysis

2. Computer Systems (Months 3-4)

Goal: Understand the hardware/software interface and system performance

Topics:

• Computer architecture basics
• Memory hierarchy and caching
• Virtual memory and paging
• Process management and scheduling
• I/O systems and file systems
• Performance measurement and optimization

Practical Focus:

• Profile and optimize real applications
• Understand why certain code patterns are faster
• Memory management deep dive

Key Resource: Computer Systems: A Programmer's Perspective by Bryant and O'Hallaron
Project: Build a system monitoring tool that demonstrates understanding of OS concepts

3. Data Structures and Database Systems (Months 5-6)

Goal: Theoretical foundations of data organization and management

Topics:

• Advanced tree structures (B-trees, B+ trees)
• Hash tables and collision resolution
• Relational model and SQL theory
• Database design and normalization
• Query optimization
• Transaction processing and ACID properties
• NoSQL concepts and CAP theorem

Practical Focus:

• Design database schemas for complex domains
• Implement a simple database engine
• Compare SQL vs NoSQL trade-offs

Key Resource: Database System Concepts by Silberschatz, Korth, Sudarshan
Project: Build a mini-database with indexing and basic query optimization

4. Software Engineering Theory (Months 7-8)

Goal: Academic perspective on software development lifecycle

Topics:

• Software architecture patterns and styles
• Design patterns and their formal properties
• Software testing theory and verification
• Software metrics and measurement
• Formal methods basics
• Software project management models

Practical Focus:

• Apply architectural patterns to large systems
• Implement comprehensive testing strategies
• Analyze and refactor legacy codebases

Key Resource: Software Architecture in Practice by Bass, Clements, Kazman
Project: Architecture analysis and redesign of an existing system

5. Programming Languages (Months 9-10)

Goal: Understand language design principles and implementation

Topics:

• Language paradigms (imperative, functional, object-oriented, logic)
• Syntax and semantics
• Type systems and type checking
• Memory management (garbage collection, reference counting)
• Compiler basics (lexing, parsing, code generation)
• Language feature analysis

Practical Focus:

• Compare languages you use professionally
• Implement interpreters for simple languages
• Understand performance implications of language features

Key Resource: Programming Language Pragmatics by Scott
Project: Build an interpreter for a small functional language

6. Computer Networks (Months 11-12)

Goal: Understanding distributed systems and network programming

Topics:

• Network protocols (TCP/IP, HTTP, DNS)
• Network performance and reliability
• Distributed system fundamentals
• Consensus algorithms (Raft, Paxos basics)
• Microservices and service architecture
• Security in distributed systems

Practical Focus:

• Network programming and protocol implementation
• Build distributed applications
• Analyze network performance

Key Resource: Computer Networks by Tanenbaum and Wetherall
Project: Implement a distributed key-value store with consensus

Electives (Months 13-18)

Choose 3 courses based on interests and career goals

Option A: Machine Learning

Focus: Practical ML with conceptual understanding

Topics:

• Supervised learning (regression, classification)
• Unsupervised learning (clustering, dimensionality reduction)
• Model evaluation and validation
• Neural networks and deep learning basics
• Feature engineering and selection
• ML system design and deployment

Project: End-to-end ML system for a real-world problem

Option B: Information Security

Focus: Security principles and implementation

Topics:

• Cryptography fundamentals
• Network security protocols
• Application security (OWASP Top 10)
• System security and hardening
• Security testing and code review
• Privacy and data protection

Project: Security audit and hardening of a web application

Option C: Human-Computer Interaction

Focus: User-centered design and interface principles

Topics:

• User experience design principles
• Usability testing and evaluation
• Accessibility and universal design
• Interface design patterns
• Mobile and web interface design
• User research methods

Project: Complete UX redesign of an existing application

Option D: Computer Graphics

Focus: 3D graphics and visualization

Topics:

• 3D transformations and projections
• Rendering pipelines
• Lighting and shading models
• Texture mapping
• Computer animation basics
• Graphics programming (OpenGL/WebGL)

Project: 3D graphics application or game engine component

Option E: Artificial Intelligence

Focus: AI algorithms and reasoning

Topics:

• Search algorithms (A*, minimax)
• Knowledge representation
• Logic and reasoning systems
• Planning algorithms
• Natural language processing basics
• Computer vision fundamentals

Project: AI system for game playing or problem solving

Option F: High-Performance Computing

Focus: Parallel and distributed computing

Topics:

• Parallel programming models
• GPU computing (CUDA/OpenCL)
• Distributed computing frameworks
• Performance optimization techniques
• Scalability analysis
• Cloud computing architectures

Project: Parallel implementation of a computationally intensive algorithm

Capstone Experience (Months 19-24)

Option 1: Research Project

Work on a novel problem in your area of interest

• Literature review
• Problem definition and approach
• Implementation and evaluation
• Written report and presentation

Option 2: Industry Collaboration

Partner with a company on a real-world problem

• Requirements analysis
• System design and architecture
• Implementation and testing
• Deployment and maintenance plan

Option 3: Open Source Contribution

Make significant contributions to an established project

• Codebase analysis and understanding
• Feature development or performance improvement
• Documentation and testing
• Community engagement

Study Approach

Leveraging Your Experience

• Connect Theory to Practice: Relate every concept to systems you've built
• Architecture Focus: Emphasize how concepts apply to large-scale systems
• Performance Awareness: Always consider scalability and efficiency
• Real-World Examples: Use actual systems and problems from your experience

Learning Methods

• Implementation-Heavy: Build working systems to understand concepts
• Comparative Analysis: Compare different approaches and trade-offs
• Case Studies: Analyze how major companies solve similar problems
• Code Reading: Study implementations from successful open source projects

Assessment Strategy

• Portfolio Development: Build a portfolio demonstrating each concept
• Technical Writing: Document your learning and insights
• Peer Discussion: Engage with other developers and CS students
• Conference Attendance: Attend relevant conferences and meetups

Recommended Resources

Core Textbooks

• Introduction to Algorithms (CLRS) - algorithms reference
• Operating System Concepts by Silberschatz - systems foundation
• Computer Networks by Tanenbaum - networking fundamentals
• Database System Concepts by Silberschatz - data management

Online Resources

• MIT OpenCourseWare (6.006, 6.033, 6.034)
• Stanford CS courses (CS161, CS144, CS145)
• UC Berkeley CS courses (CS61B, CS162, CS186)
• Coursera/edX offerings from top universities

Practical Platforms

• LeetCode/HackerRank for algorithm practice
• GitHub for project hosting and code review
• Cloud platforms (AWS/GCP/Azure) for systems projects
• Kaggle for machine learning projects

Timeline Flexibility

Accelerated Path (12-15 months):

• Skip topics you're already strong in
• Focus on 2-3 core areas plus 2 electives
• Combine related projects

Extended Path (24-30 months):

• Take time to deeply explore each topic
• Add additional electives
• Include internship or industry collaboration

Maintenance Phase (Ongoing):

• Stay current with research trends
• Contribute to open source projects
• Attend conferences and workshops
• Consider teaching or mentoring others

Success Metrics

Knowledge Indicators

• Can explain CS concepts to other developers
• Able to make informed architectural decisions
• Understand trade-offs in technology choices
• Can read and critique academic papers

Practical Outcomes

• Portfolio of implemented systems
• Contributions to open source projects
• Technical blog posts or presentations
• Network of academic and research contacts

Career Impact

• Qualified for senior architect or CTO roles
• Able to lead technical research initiatives
• Prepared for potential PhD studies
• Enhanced credibility in technical discussions