Milestone 1: Complexity & Big O Foundations

🟦 Milestone 1: Complexity & Big O Foundations

Before you can build fast systems, you must know how to measure “Speed” in a way that doesn’t depend on your computer’s hardware.

📚 Slow-Paced Deep Dives (University Modules)

• Module 1: Big O Notation (The Speed Limit): DSA-101. Measuring efficiency across different datasets.
• Module 2: Time vs Space (The Trade-off): DSA-102. Deciding between memory usage and execution speed.

🥅 Milestone Goals

1. Explain why Big O ignores constants and focuses on trends.
2. Identify the difference between O(1), O(n), and O(n²).
3. Analyze the Space Complexity of a simple algorithm.
4. Distinguish between Best, Average, and Worst Case scenarios.

:::tip Beginner Start Think of Big O as The Growth Rate. It doesn’t tell you how many seconds a program takes, but how many more steps it will take when you double the data! :::