Computer Science Fundamentals
Beginner
Big O Notation
A focused course on complexity notation alone — working through O(1), O(log n), O(n), O(n log n), O(n²) and beyond, plus Big Omega and Big Theta, then applying each to the operations of real data structures.
30 chapters
7h 30m
What you'll learn
- Introduction to Big O Notation
- Why Algorithm Efficiency Matters
- Understanding Time Complexity
- Understanding Space Complexity
- Best Case, Average Case, and Worst Case
- Constant Time Complexity O(1)
- Linear Time Complexity O(n)
- Logarithmic Time Complexity O(log n)
Course content
30 chapters · 7h 30m- 1 Introduction to Big O Notation 15 min
- 2 Why Algorithm Efficiency Matters 15 min
- 3 Understanding Time Complexity 15 min
- 4 Understanding Space Complexity 15 min
- 5 Best Case, Average Case, and Worst Case 15 min
- 6 Constant Time Complexity O(1) 15 min
- 7 Linear Time Complexity O(n) 15 min
- 8 Logarithmic Time Complexity O(log n) 15 min
- 9 Linearithmic Complexity O(n log n) 15 min
- 10 Quadratic Complexity O(n²) 15 min
- 11 Cubic Complexity O(n³) 15 min
- 12 Exponential Complexity O(2ⁿ) 15 min
- 13 Factorial Complexity O(n!) 15 min
- 14 Big Omega and Big Theta Notation 15 min
- 15 Asymptotic Analysis Fundamentals 15 min
- 16 Complexity Analysis of Arrays 15 min
- 17 Complexity Analysis of Strings 15 min
- 18 Complexity Analysis of Linked Lists 15 min
- 19 Complexity Analysis of Stacks and Queues 15 min
- 20 Complexity Analysis of Hash Tables 15 min
- 21 Complexity Analysis of Trees 15 min
- 22 Complexity Analysis of Graph Algorithms 15 min
- 23 Complexity Analysis of Sorting Algorithms 15 min
- 24 Complexity Analysis of Searching Algorithms 15 min
- 25 Recursive Complexity Analysis 15 min
- 26 Space-Time Tradeoffs 15 min
- 27 Optimizing Algorithms for Better Complexity 15 min
- 28 Real-World Applications of Big O 15 min
- 29 Big O Interview Preparation 15 min
- 30 Final Projects and Complexity Optimization Challenges 15 min