Space Complexity Explained | Master Big O Notation

Space Complexity Explained | Master Big O Notation Want to understand how much memory your code really uses? In this video, we break down Big O Notation and Space Complexity using simple examples, recursion, and real-world tradeoffs. You'll learn how to measure memory usage, understand the difference between stack and heap memory, and see how engineers balance speed and memory when designing scalable systems. This video is a continuation of Time Complexity Explained. If you haven't watched that video yet, I strongly recommend starting there first. 📌 What you will learn in this video • What Space Complexity is • Difference between Time Complexity and Space Complexity • Heap vs Stack memory • Why input size is not counted in Space Complexity • O(1) Space Complexity explained • O(n) Space Complexity explained • Recursive stack growth • In-place vs extra-memory solutions • Time-Space Tradeoffs 👶 Who is this video for? • Beginners learning Data Structures & Algorithms • Students preparing for coding interviews • Anyone who wants to write memory-efficient code 🚀 Why this video is different • Intuition-first explanation • Visual understanding of memory • Real-world examples • Step-by-step breakdown • Focus on clarity over complexity 📌 Previous video in the series: Time Complexity Explained 📌 Next topic: Data Structures 💬 Have questions or suggestions? Comment below — I reply to every comment! 🔔 Subscribe for more DSA and programming content. #BigO #SpaceComplexity #DSA #Algorithms #CodingInterview #Programming #LearnToCode #SoftwareEngineering #Python #DataStructures