Computer Science 101: Master the Theory Behind Programming Course Syllabus

Overview: This course provides a concise yet comprehensive introduction to core computer science theory essential for writing efficient, scalable code. Designed for beginners, it spans approximately 6 hours of focused learning, divided into 8 modules. You'll explore foundational topics like computer architecture, data structures, algorithms, and complexity analysis using language-agnostic explanations, pseudocode, and diagrams. Each module builds theoretical understanding with practical insights, preparing you to tackle real-world programming challenges and technical interviews. While no hands-on coding labs are included, the course emphasizes conceptual clarity and problem-solving strategies applicable across programming languages.

Module 1: Introduction to Computer Science & Architecture

Estimated time: 0.5 hours

• CPU, memory hierarchy, and instruction execution cycle
• Von Neumann architecture
• Binary representation and data encoding

Module 2: Data Structures Fundamentals

Estimated time: 1 hours

• Arrays vs. linked lists: trade-offs in access and manipulation
• Implementing stacks for LIFO operations
• Implementing queues for FIFO operations

Module 3: Algorithm Analysis & Big O

Estimated time: 0.75 hours

• Measuring performance: best, average, and worst-case scenarios
• Big O notation rules for common operations
• Time and space complexity evaluation

Module 4: Sorting & Searching Algorithms

Estimated time: 1 hours

• Implementing bubble and insertion sort
• Implementing merge and quick sort
• Binary search on sorted arrays
• Logarithmic complexity of binary search

Module 5: Trees & Graphs

Estimated time: 1 hours

• Binary trees and tree properties
• Tree traversals: in-order, pre-order, post-order
• Graph representations
• Graph traversal algorithms: DFS and BFS

Module 6: Hashing & Hash Tables

Estimated time: 0.75 hours

• Hash functions
• Collision resolution: chaining and open addressing
• Use cases for constant-time lookup and caching

Module 7: Recursion & Dynamic Programming

Estimated time: 0.75 hours

• Recursive problem decomposition
• Call stack behavior in recursion
• Memoization patterns
• Bottom-up dynamic programming for optimization

Module 8: Putting It All Together & Best Practices

Estimated time: 0.5 hours

• Designing end-to-end algorithmic solutions for sample problems
• Understanding trade-offs in design choices
• Code readability and choosing the right abstraction

Prerequisites

• Familiarity with basic programming concepts
• No prior computer science degree required
• Willingness to engage with pseudocode and diagrams

What You'll Be Able to Do After

• Understand fundamental computer science concepts: algorithms, data structures, and computational complexity
• Grasp how memory, CPU, and I/O interact in program execution and operating systems
• Analyze and design efficient algorithms for sorting, searching, and graph traversal
• Apply key data structures—arrays, linked lists, stacks, queues, trees, and hash tables—in code
• Evaluate time and space complexity using Big O notation for real-world problem solving