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! 🔥 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 #geekific #bigOnotation #codinginterview #timecomplexity #spacecomplexity