Big-O Notation Explained | Time & Space Complexity in Programming | Geekific Remastered

Understanding Big-O notation is crucial for writing efficient code and acing coding interviews! In this video, we break down the fundamentals of time and space complexity in a simple yet comprehensive way.

What you’ll learn:
🧠 Memory Analysis: How to analyze the memory (space complexity) of a program
⏳ Time Complexity: How execution time scales with input size
⚠️ Worst-Case Focus: Why we prioritize worst-case scenarios in complexity analysis
📊 Common Complexities: O(1), O(n), O(n²), O(log n), and O(n log n)
⚖️ Time-Space Trade-off: When to optimize for speed vs memory

By the end of this video, you’ll have a solid grasp of how to measure algorithm efficiency, making you a better problem solver and a stronger programmer!

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💡 Chapters:
00:00 Introduction
00:07 Understanding Space Complexity
00:50 Breaking Down Time Complexity
01:14 Worst-Case Analysis & Big-O Notation
02:59 Quadratic vs Linear Complexity
04:00 Time-Space Trade-Off in Coding
05:48 Logarithmic Complexity Explained
08:30 Thanks for Watching!

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