The theory behind it surely is not a day to day topic, but I think the ability to know the rough time and space complexity of an algorithm you've just written is super important.
It's definitely one of those topics that appears complex and mysterious until you familiarize yourself enough with it to realize that it's basically just a notation for eye-balling the relative growth patterns and performance of comparable algorithms.
I dabbled in CS before ultimately not pursuing it in college. Not every CS concept is critical professionally, but from what I learned at the time, this is a topic that has proven to be a big part of letting me intuit solutions in my day-to-day.
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Algorithm complexity things (i.e Big O)
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The theory behind it surely is not a day to day topic, but I think the ability to know the rough time and space complexity of an algorithm you've just written is super important.
It's definitely important, I just couldn't understand how to do it. But, I will learn it one day :)
It's definitely one of those topics that appears complex and mysterious until you familiarize yourself enough with it to realize that it's basically just a notation for eye-balling the relative growth patterns and performance of comparable algorithms.
I dabbled in CS before ultimately not pursuing it in college. Not every CS concept is critical professionally, but from what I learned at the time, this is a topic that has proven to be a big part of letting me intuit solutions in my day-to-day.