Time Complexity Explained for Beginners | Hello DSA Series

⏱️ Time Complexity — one of the most important (and confusing) topics in DSA — explained in the simplest way possible! In this episode of the **Hello DSA** series, I break down what **time and space complexity** really mean, why they matter, and how you can understand Big O Notation intuitively — without memorizing formulas. By the end of this video, you’ll be able to: ✅ Understand how algorithms are measured for efficiency ✅ Read and interpret Big O notations (O(1), O(n), O(log n), etc.) ✅ Visualize time complexity with practical examples ✅ Differentiate between Time and Space Complexity ✅ Apply this understanding to real coding problems -------------------------------------- 🎥 Part of the **Hello DSA Series**, where we make Data Structures & Algorithms easy, visual, and fun to learn — one concept at a time. 💻 GitHub Code + Notes: https://github.com/mayank953/Youtube/tree/main/Hello%20DSA/Time%20Complexity/Time%20and%20Space%20Complexity -------------------------------------- ⏱️ Timestamps: -------------------------------------- 📚 More in the Hello DSA Series: - Merge Sort Explained Intuitively - Quick Sort Visualization + Python Code - Recursion for Beginners -------------------------------------- 👨‍💻 Connect with Me: LinkedIn: https://www.linkedin.com/in/mayank953/ YouTube: https://www.youtube.com/@tech.mayankagg Instagram: https://www.instagram.com/tech.mayankagg/ Substack: https://aiwithmayank.substack.com Medium: https://medium.com/@tech.mayankagg Udemy: https://www.udemy.com/user/mayank-aggarwal-197/ GitHub: https://github.com/mayank953/ -------------------------------------- #TimeComplexity #HelloDSA #BigONotation #CodingInterviews #DSA #TechWithMayank