Why We Need Big O Notation?

The lecture discusses the limitations of using clock 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 time. Practical examples using for and while loops demonstrate the variability of execution time across different systems. The lecture concludes by emphasizing the importance of Big O notation for performance rating and hints at future discussions on space complexity.

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