I second Big O notation and this whole idea. I didn't do an entire computer science degree, but I did some, and these concepts are what have stuck with me as considerations that come up a lot.
Time to write my Big O practical applications essay. Still need to fully understand it though. I read a great book about CS concepts written by someone who didn't have a CS degree. Not an ad but a good book.
I know it is expensive and massive but I really think every developer should have a copy of CLRS. It is well organized, well written, and extremely thorough. In fact I was just paging through it today to review a few graph algorithms.
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Big O notation, an understanding of what algorithms are "expensive," and an understanding of what happens with "expensive" algorithms at large scales.
I second Big O notation and this whole idea. I didn't do an entire computer science degree, but I did some, and these concepts are what have stuck with me as considerations that come up a lot.
Time to write my Big O practical applications essay. Still need to fully understand it though. I read a great book about CS concepts written by someone who didn't have a CS degree. Not an ad but a good book.
bigmachine.io/products/the-imposte...
Ah, someone beat me to it I see :)
I couldn't agree more about this recommendation.
For some reason at my CS degree we hardly touched on Big O. Does anyone have some great resources for gaining a better understanding?
I know it is expensive and massive but I really think every developer should have a copy of CLRS. It is well organized, well written, and extremely thorough. In fact I was just paging through it today to review a few graph algorithms.