Next Meetup

"Big O" Notation...for the Uninitiated
Ever had a feeling that those triple nested loops in your code weren't such a good idea, but couldn't quite pinpoint why? Ever had to just nod and smile when someone described a sorting algorithm as being of the order "n log n"? Want to irrefutably *prove* whether your (or someone else's) code does or doesn't scale? (And if we're honest with ourselves, I think we all know the answer to that question...) If you answered "yes" to any of those questions, or if you just want to hang out with the coolest group of computer scientists in the greater Nashville area, then this Computer Science Meetup is certainly one not to miss! The purpose of this meeting is to provide a crash course on so-called "Big O" notation. Basically, "Big O" notation is a nomenclature for describing the complexity of an algorithm by describing its runtime as a function of its input size. For example, you might have heard two nested loops that both iterate over an array of size n, described as "Big O of n-squared" or O(n^2) because there are exactly n*n operations happening inside the body of the nested loop structure. Hence, as the size of n grows, the runtime of the nested loops grows *much* faster -- it grows at a rate of n*n to be exact. (Try graphing y=x^2 in Wolfram Alpha to see why this might not be a desirable algorithmic complexity...or reminisce back to Algebra class when the teacher was talking about parabolas as part of the unit on polynomial functions.) A specific date and location is still being worked out, but the expectation/hope is that it will happen in the mid- to late-September timeframe somewhere between Nashville and Franklin. It would be especially helpful if you'd leave a thought or comment with your ideas, questions, concerns, or suggestions for meeting places/times so that we can maximize the attendance and learning opportunity for as many folks as possible.

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