Heap-6 | Time & Space Complexity | 🔥 Beginner, Medium & Advanced Level

Telegram Channel : https://t.me/ctobhaiya_tg Instagram: https://www.instagram.com/ctobhaiya Linkedin: https://www.linkedin.com/in/anuj-kumar-a-k-a-cto-bhaiya-on-youtube-9a188968 Github: https://github.com/team-codebug/babua-dsa-patterns-course Support me 🙌🏻: https://www.buymeacoffee.com/anuj.baranwal.1994 Leetcode: X Github Repo: https://github.com/team-codebug/leetcode Notes: https://github.com/team-codebug/leetcode/blob/main/DSA_In_90Days/10_Heap/Heap_6_Time_%26_Space_Complexity.svg 3 Months DSA for Placements! 🚀 Beginner to Advanced Playlist : 📊 Heap Time & Space Complexity Explained + Why Build Heap is O(n), Not O(n log n) In this video, we go beyond code and dive into the theoretical analysis of heap operations. Learn how insertion, deletion, and heapify work in terms of time and space complexity, and why the often-misunderstood Build Heap operation actually runs in O(n) time — not O(n log n) as it may seem! 🔍 What’s covered: ⏱ Time complexities of insert, delete, and heapify 🧠 Space complexity for heap implementations 📉 Depth-based analysis of nodes in a complete binary tree 🔣 Mathematical proof: T(n) = ∑ (n / 2^h) * h = O(n) 🧪 Why starting from the last non-leaf node is the key to optimization 🤔 Common misconceptions around heap build time This is a must-watch for anyone preparing for interviews, brushing up on DSA fundamentals, or simply curious about the math behind heaps! 🎯 Don’t forget to like, comment your doubts, and subscribe for more deep-dive theory videos. #HeapComplexity #BuildHeapON #DSAAnalysis #CTOBhaiya #HeapifyProof =========================== ➡️ Connect with me: LinkedIn : https://www.linkedin.com/in/anuj-kumar-a-k-a-cto-bhaiya-on-youtube-9a188968 Telegram Channel : https://t.me/ctobhaiya_tg Instagram: https://www.instagram.com/ctobhaiya ===========================