Honestly, I don't think calculating the O(f(n)) is ever worth it outside of academia. Intuition is only slightly worse, and it is just so much time-ch

Honestly, I don't think calculating the O(f(n)) is ever worth it outside of academia. Intuition is only slightly worse, and it is just so much time-cheaper. Sure. I've never been asked for a formal mathematical proof of the Big-O complexity of my algorithms in an interview. And, as an interviewer, I've never asked. Intuition is exactly what interviews are looking for. If an algorithm has a nested for loop, and the inner loop is traversing the full data set, you should be able to say, "Oh, yeah, that looks like O(n^2) time complexity." If an algorithm is storing every element of a data-set in memory, you should be able to say, "That's O(n) space complexity." Big-O notation, in practice, is a handy shorthand for talking about various classes of algorithms, and ranking them in order of how much time/space they take.