The purpose of this course is to provide a unified mathematical and technical framework that empowers students to transition from high-level problem solvers to systems architects. In an era where software, data management, and hardware infrastructure are increasingly interdependent, a fragmented understanding of computing is no longer sufficient.

CLO 1. Apply Mathematical Logic: Use discrete structures, linear algebra, and formal logic to verify algorithm correctness and model complex computational systems.

CLO 2. Architect Data Systems: Design and implement optimized data structures and relational databases to manage large-scale information efficiently and securely.

CLO 3. Analyze System Infrastructure: Evaluate the interaction between operating system resource management and network protocols to optimize software performance and security.

CLO 4. Engineer Intelligent Solutions: Execute the full software development life cycle—from requirement gathering to deployment—while integrating machine learning models to solve non-deterministic challenges.

Theoretical Foundations and Logic

• Discrete Structures: Propositional logic, sets, relations, and graph theory for modeling connectivity.
• Linear Algebra: Matrix operations, vector spaces, and transformations used in data science and computer graphics.
• Computational Complexity: Big-O notation, time-space trade-offs, and formal proof techniques.

Data Management and Algorithmic Design

• Data Structures: Implementation of linear (stacks, queues, linked lists) and non-linear (trees, heaps, hash tables) structures.
• Advanced Algorithms: Sorting, searching, recursion, and dynamic programming.
• Database Systems: ER-modeling, SQL query optimization, normalization, and ACID transaction properties.

Core Systems and Networking

• Operating System Architecture: Process scheduling, concurrency, deadlocks, and virtual memory management.
• Network Protocols: The OSI model, TCP/IP suite, routing, and switching.
• System Security: Principles of cryptography, firewalls, and securing distributed environments.

Software Engineering and Artificial Intelligence

• Software Development Life Cycle (SDLC): Agile and Scrum methodologies, requirement engineering, and UML modeling.
• Quality Assurance: Unit testing, version control with Git, and CI/CD pipelines.
• Intelligent Systems: Heuristic search, supervised and unsupervised learning, and the ethical deployment of AI.