In this video, we continue our algorithmic journey with Big O. Building on the previous introduction to Big O, we take a deep dive into sorting algorithms– specifically the Bubble Sort Algorithm. I’ll break down how it works and my understanding of how to measure its performance using Big O. I will also create a Python function that implements this algorithm. By the end of this video, you will have a solid understanding of the Bubble sort Algorithm and see why it’s an inefficient algorithm.
📌 Key Topics Covered
- Big O notation recap
- Introduction to the Bubble Sort algorithm
- How Bubble Sort works (step-by-step walkthrough)
- Python implementation with detailed code explanation
- Performance analysis using Big O
- Time complexity (worst case)
- Comparing Bubble Sort with other sorting algorithms (brief overview)
- Common pitfalls and optimization tips
📊Why does this matter?
Understanding sorting algorithms is crucial for writing efficient code and optimizing performance. While Bubble Sort might not be the fastest kid on the block, analysing its complexity using Big O notation helps build a solid foundation for tackling more advanced algorithms down the road. Plus, implementing it in Python will sharpen your problem-solving skills and deepen your understanding of algorithm efficiency.
👉 Missed the Big O intro? Check it out
https://youtu.be/DCAynmt_znw to get up to speed!
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GitHub: https://github.com/OTeeEnabor/OpenDelta_DSA/tree/main/big_oh/1
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