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I really hope you are saying this tongue in cheek :) I agree that tech interviews are broken but you can't say big O is rubbish for all coding jobs. Yes, when
by eranation 11y ago
I really hope you are saying this tongue in cheek :)
I agree that tech interviews are broken but you can't say big O is rubbish for all coding jobs. Yes, when I was doing web development it was true most of the time (even then knowing the difference between scanning a list or lookup in a map/dictionary was helpful) but now I'm working on machine learning, big data, and it involves a lot of graph algorithms. All this academic rubbish is stuff that I need to do my job. The fact you can get a great paying job in mobile and web based on street cred is wonderful! This is how I got my first job without any official CS education. But I actually need to know how to prove an algorithm I developed is linear, I need to understand all known graph algorithms to know I'm not reinventing the wheel. For web and mobile and also enterprise you are right. For building self driving cars, search engines, databases, machine learning and more, you kind of ought to know all that big O, data structures and algorithm rubbish.
- RogerL 11y agoThe concept is not rubbish, the interviewing technique is. I can think of any number of people that can rattle off what O(n) means, yet inevitably write O(N^3) code given half a chance. And vice versa. And another group that forget the constants that big oh notation throws away very often really do matter. Talk about efficiency, sure, why not? Throw a piece of code at somebody with convoluted terminating conditions on a loop and ask for the big oh, under pressure, while being worked? Rubbish.