Computer Science 101: Master the Theory Behind Programming

Module 1: Introduction to Computer Science & Architecture

⏳ 30 minutes

• CPU, memory hierarchy, and instruction execution cycle

• Von Neumann architecture, binary representation, and data encoding

Module 2: Data Structures Fundamentals

⏳ 1 hour

• Arrays vs. linked lists: trade-offs in access and manipulation

• Implementing stacks and queues for LIFO/FIFO operations

Module 3: Algorithm Analysis & Big O

⏳ 45 minutes

• Measuring performance: best, average, and worst-case scenarios

• Big O notation rules for common operations

Module 4: Sorting & Searching Algorithms

⏳ 1 hour

• Implementing and comparing bubble, insertion, merge, and quick sort

• Binary search on sorted arrays and its logarithmic complexity

Module 5: Trees & Graphs

⏳ 1 hour

• Binary trees, traversals (in-/pre-/post-order), and tree properties

• Graph representations and traversal algorithms: DFS and BFS

Module 6: Hashing & Hash Tables

⏳ 45 minutes

• Hash functions, collision resolution (chaining, open addressing)

• Use cases for constant-time lookup and caching

Module 7: Recursion & Dynamic Programming

⏳ 45 minutes

• Recursive problem decomposition and call stack behavior

• Memoization patterns and bottom-up DP for optimization

Module 8: Putting It All Together & Best Practices

⏳ 30 minutes

• Designing end-to-end algorithmic solutions for sample problems

• Trade-offs, code readability, and choosing the right abstraction

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