Data Structures and Algorithms The Complete Masterclass - Why We Need Big O Notation?

The lecture discusses the limitations of using system time to measure code performance due to hardware dependencies. It introduces Big O notation as a more reliable metric for evaluating algorithm efficiency, focusing on the number of operations rather than execution time. Practical examples using for and while loops to sum natural numbers illustrate the variability of time measurements across different systems. The lecture concludes by emphasizing the importance of Big O for performance rating and hints at future discussions on space complexity.

What is the significance of comparing timestamps for different implementations?

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Explain why Big O notation is considered a more reliable measure of algorithm performance than execution time.

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What are the limitations of calculating time for each program based on hardware?

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