Data Structure and Algorithm

1) Same problem → different approaches

2) Introduction to Time Complexity (Big-O)

• O(1), O(n), O(n²) — concept only

3) How to break a problem into steps

• Problem → logic → data structure → algorithm

Asymptotic notation is a way to describe how an algorithm’s performance grows as the input size becomes very large. Instead of measuring exact execution time, it focuses on the rate of growth. Common notations are Big-O (O) for worst-case, Big-Ω (Ω) for best-case, and Big-Θ (Θ) for average or tight bound.
We use asymptotic notation because actual running time depends on hardware, language, and environment. Asymptotic analysis removes these variables and helps us compare algorithms objectively, predict scalability, and choose efficient solutions for large datasets—something crucial in real-world systems and technical interviews.

In modern systems, time complexity measures how execution time grows with input size, while space complexity measures additional memory usage. With cloud-scale data and high-traffic applications, efficient algorithms reduce latency and memory costs. Asymptotic analysis helps design scalable, cost-effective, and performance-optimized solutions independent of machines or platforms.