Time Complexity Explained with Loops and Data Structures – Master Big O

Time complexity is one of the most fundamental concepts in computer science and DSA (Data Structures and Algorithms). In this video, we break down everything you need to know about how the performance of your code changes as the input size grows. If you’ve ever wondered why your loop is slow or how to optimize your algorithm for better performance, this video is the complete guide for you.

We start with an introduction to Big O Notation, the standard way of expressing time complexity. You'll learn the common complexities like:

O(1) – Constant Time

O(log N) – Logarithmic Time

O(N) – Linear Time

O(N log N) – Linearithmic Time

O(N²) – Quadratic Time

We then dive deep into loop analysis — single, nested, and logarithmic loops — and show how to calculate their time complexities step-by-step. With multiple C++ loop examples and visual dry runs, you’ll understand how the number of operations depends on ‘n’, the size of the input.

Next, we explore how different data structures behave in terms of time:

Data Structure Access Insert Delete
Array O(1) O(n) O(n)
Linked List O(n) O(1) O(1)
Stack O(n) O(1) O(1)
Queue O(n) O(1) O(1)

With real-world analogies and coding patterns, we explain the value of ‘c’ and ‘n’ in time complexity and how to calculate operations accurately.

This topic is crucial for coding interviews, competitive programming, and university DSA finals. You’ll walk away with the confidence to analyze any code snippet's time complexity like a pro!

Видео Time Complexity Explained with Loops and Data Structures – Master Big O канала Laiba Zahoor