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Session 12
Algorithm and Programming (Python)

Algorithm Analysis and Complexity Theory

4 hours
Duration
8
Materials
6
Objectives
Session Overview

Comprehensive study of algorithm analysis including time and space complexity, Big O notation, asymptotic analysis, and algorithm performance evaluation.

Learning Objectives
By the end of this session, you should be able to:
  • Master Big O, Omega, and Theta notation with mathematical rigor
  • Analyze time and space complexity of algorithms and data structures
  • Apply amortized analysis for average-case complexity evaluation
  • Compare and evaluate different algorithmic approaches
  • Understand NP-completeness and computational complexity classes
  • Design experiments to measure and validate algorithmic performance
Course Materials
Download materials for offline study and reference
Algorithm Analysis Theory and Practice (50 pages)
Available material
Big O Notation and Asymptotic Analysis Guide
Available material
Complexity Analysis Examples and Case Studies
Available material
Amortized Analysis Techniques
Available material
NP-Completeness and Complexity Classes
Available material
40 Algorithm Analysis Problems
Available material
Performance Measurement and Benchmarking
Available material
Complexity Theory Applications
Available material