Data analysis, statistics, and mathematics have been revolutionized by increases in computing power -- and now it's happening again. We can go beyond traditional statistical approaches to data by using unsupervised learning techniques and advanced math.

Topological data analysis looks at the shape of data -- the underlying structure -- and in revealing surprising insights, it also brings up interesting questions. The best part is that you can do your own analysis with Python and R, free and open-source tools. We'll look at real-life applications of topological data analysis from "the dieting paradox" in human health to the health of financial networks, and I'll ask you how topology might apply to your big data problems!

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