Master Big-O Notation: Simplified Guide to Time & Space Complexity 🚀

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!

🔥 Our Discord, GitHub repo, and socials: https://linktr.ee/geekific

💡 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!

▶️ If you found this video helpful, check other Geekific uploads:
- SOLID Principles and Best Practices : https://youtu.be/HoA6aZPR5K0
- Introduction to Dynamic Programming : https://youtu.be/IjpoE28Ii34
- Trees Compared and Visualized : https://youtu.be/hmSFuM2Tglw
- Generics and Wildcards in Java : https://youtu.be/FXAUXvPNKi8
- Sorting Algorithms Complexities: https://youtu.be/UqmKiz2P0Lw
- Domain-Driven Design Made Simple : https://youtu.be/H5--9pMmuK4
- Clean Architecture with Spring Boot: https://youtu.be/pv-qFt69Bng

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