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Ironically, big-O describes things that are very easy to understand, in a notation that looks complicated.

If you have a lot of experience, you probably already understand the concepts. The notation just gives a clear way to describe them: https://rob-bell.net/2009/06/a-beginners-guide-to-big-o-nota...,




True, but studying the theory gives a better intuition for big-O. And theory is helpful for some of the edge cases that can be more complicated. For instance, I wouldn't want to try to determine the run-time of a recursive algorithm with just the info in that article.


Indeed, and if you did learn these things through experience, as I supposed, then you probably did so by writing something O(2^n) and suffering when you had to process >100,000 records.

(Why does it take 100 ms for 10,000 records and 50 minutes for 100,000? ... hmm)

Learning these concepts through the theory is definitely more efficient.




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