Big O Notation and Algorithm Analysis - Chapter 3, Lecture 6

Algorithm Analysis | Time Complexity of an Algorithm | Space Complexity of an Algorithm |
Big O Notation Explained with Examples | Chapter 3 – Algorithm and Problem Analysis | 1st Year Computer Science

In this video, we explain Big O Notation, a key concept in measuring the time complexity and space complexity of algorithms. This topic is part of Chapter 3: Algorithm and Problem Analysis from the 1st Year Computer Science syllabus.

🔍 What You'll Learn:

What is Big O Notation?

Why we use Big O to analyze algorithm efficiency

Detailed explanation of:

O(1) – Constant Time

O(n) – Linear Time

O(n²) – Quadratic Time

O(log n) – Logarithmic Time

Real-life examples using student-based scenarios

How algorithm performance changes with input size

How to compare different algorithms based on runtime

This video is designed for beginners and students to understand algorithm analysis with clear, easy-to-follow examples that help visualize how different complexities work in real-world situations.

🎓 Perfect for:

1st Year Computer Science Students

Students preparing for board exams

Beginners learning about algorithms

Anyone who wants to understand how runtime grows with input size

📚 Covered Topics:
Big O Notation, Time Complexity, Space Complexity, Algorithm Efficiency, Constant Time O(1), Linear Time O(n), Quadratic Time O(n²), Logarithmic Time O(log n), Worst Case Analysis, Algorithm Growth Rate, Chapter 3 Problem Analysis.

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