overview of CS degrees

CS_DEGREE.md

Course Name	Concepts Covered	Year Covered	Prerequisites
Introduction to Programming	Programming fundamentals, variables, control structures, functions, basic problem solving, introduction to a programming language (Python/Java)	Year 1, Fall	None (entry-level course)
Object-Oriented Programming	Classes, objects, inheritance, polymorphism, encapsulation, abstract data types, basic data structures (arrays, lists)	Year 1, Spring	Introduction to Programming
Discrete Mathematics	Logic, sets, functions, relations, graph theory, combinatorics, mathematical induction, proof techniques	Year 1-2	Calculus I (may be concurrent)
Data Structures & Algorithms	Big O notation, time/space complexity, linked lists, stacks, queues, trees, hash tables, sorting algorithms, searching algorithms	Year 2, Spring	Object-Oriented Programming, Discrete Mathematics
Computer Architecture	Assembly language, CPU design, memory hierarchy, instruction sets, hardware-software interface, performance analysis	Year 2, Fall	Data Structures, Calculus II
Algorithm Analysis	Advanced complexity theory, divide-and-conquer algorithms, dynamic programming, greedy algorithms, graph algorithms, NP-completeness	Year 3, Fall	Data Structures & Algorithms, Linear Algebra
Operating Systems	Process management, memory management, file systems, concurrency, synchronization, system calls, performance optimization	Year 3, Spring	Computer Architecture, Algorithm Analysis
Database Systems	Relational model, SQL, query optimization, indexing, transaction processing, concurrency control, database design	Year 3, Fall/Spring	Data Structures & Algorithms
Computer Networks	Network protocols, TCP/IP, routing algorithms, network security, distributed systems, performance analysis	Year 3-4	Operating Systems, Probability/Statistics
Software Engineering	Software development lifecycle, design patterns, testing methodologies, version control, project management, code quality metrics	Year 3-4	Data Structures & Algorithms, database experience
Machine Learning	Supervised/unsupervised learning, neural networks, optimization algorithms, statistical learning theory, complexity analysis of ML algorithms	Year 3-4	Linear Algebra, Probability/Statistics, Algorithm Analysis
Compiler Design	Lexical analysis, parsing, semantic analysis, code generation, optimization techniques, complexity of compilation phases	Year 4	Algorithm Analysis, Programming Languages Theory
Senior Capstone	Independent project, research methodology, advanced algorithm implementation, performance evaluation, technical writing	Year 4, Fall/Spring	Major coursework completion, advisor approval