The content of this folder is info for a class on doing performance measurement, to determine Big-O of a set of algorithms, via timing of actual performance, graphing thereof, and then fitting graphs to the data points to predict, and then test, measurements of larger data for the same algorithm.
During this session, he provides a review of effective ways for approaching algorithms. Curious about how big-O works out in the real world? Wanting to understand how different algorithms and approaches behave? Wondering if it's more efficient to do a list comprehension versus building up a list another way? David will talk about some of the considerations for performance, and present ways to actually measure and compare different solutions to a problem.