Time & Space Complexity in DSA | Growth Rate, Big-O, Omega, Theta Explained for BCA & CS Students

📌 In this video, we dive deep into the most important concepts of Algorithm Analysis in Data Structures and Algorithms (DSA). You will learn:

✅ What is Growth Rate in DSA
✅ How performance of algorithms depends on Growth Rate
✅ Time Complexity and Space Complexity with simple examples
✅ The concept of Time-Space Tradeoff
✅ Introduction to Asymptotic Notations:
 👉 Big-O Notation (Upper Bound)
 👉 Big Omega Notation (Lower Bound)
 👉 Big Theta Notation (Tight Bound)
✅ Why these notations are used and how they help analyze algorithm performance

👨‍🎓 This video is Part 3 in our DSA Theory Concepts playlist, specifically designed for:
BCA students of GGSIPU, DU, MDU, and other universities
BTech / BSc Computer Science students
Students preparing for GATE, UGC-NET, and tech interviews

📺 Watch Previous Parts:
Part 1: What is Algorithm, Types & Flowchart :    • What is Algorithm in DSA? | Algorithm Prop...  
Part 2: Algorithm Analysis - Best, Worst & Average Case :    • Algorithm Analysis in DSA | Best, Worst & ...  

📘 Also explore our DSA Programs in C Playlist for line-by-line explanation of coding problems in C:
👉    • DSA Programs in C Language  

🔔 Subscribe for clear, conceptual computer science videos:    / @logiclitlearning  

Chapters:

00:00 Intro
01:00 Growth Rate
05:33 Common growth rates & effect on performance
10:49 Time Complexity, Space Complexity, Time-Space Trade off
16:14 Asymptotic Notations Big O, Theta, Omega

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