Complexity theory. Big-O. Constant, linear, logarithmic, and quadratic time versus space trade-offs. What does it actually mean when we say a function or an algorithm is efficient? How can we tell if we can do better? Join me, on this tour through a corner of computer science few developers actively think about, and you’ll walk away with a new way of looking at code and thinking about problems.


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Was a great talk, very informative.

I think more practical examples of common pitfalls would have been nice during the talk, but it gets 5 stars because a resource for that was provided.

Karl Hepler at 15:53 on 24 May 2017

Thank you!

Sandy Smith at 15:48 on 6 Jun 2017

Since I'm self-taught, I'm always insecure about CS theory. This presented the topic in a way that made it understandable and related it to what I already knew. Well done.