Big(O) Notation

Understanding the efficiency of algorithms is vital for developing efficient, scalable code that can handle real-world needs. Big O Notation is a standard method to express how an algorithm behaves with input size.

This lecture covers essentials: Constant O(1), Linear O(n), Quadratic O(n^2), Logarithmic O(log n), Exponential O(2^n) — with practical examples, common pitfalls, and tips for interviews and projects. In contrast to ignoring efficiency, knowing Big O helps avoid slow code when data gets big.

Article / Repo: https://github.com/Danesh-CS-Club/big-o-notation
Recorded Conference: [Coming soon / Insert YouTube link]
Lecture given by: Ryan Samaeian