Whether your goal is to write data intensive software, use existing software to analyze large, high dimensional data sets, or to better understand and interact with the experts who do these things, you will need a strong understanding of the structure of data and how one can try to understand it. On this blog, I plan to explore and explain the basic ideas that underlie modern data analysis from a very intuitive and minimally technical perspective: by thinking of data sets as geometric objects.
When I began learning about machine learning and data mining, I found that the intuition I had formed while studying geometry was extremely valuable in understanding the basic concepts and algorithms. But in many of the resources I’ve seen, this relatively simple geometry is hidden behind enough equations and algorithms to intimidate all but the most technically inclined readers. My goal in writing this blog is to put the geometry first, and show that anyone can gain an intuitive understanding of modern data analysis.
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About the Author: Jesse Johnson is a former math professor, with a research background in low-dimensional geometry/topology, who is now a software engineer at Google in Cambridge, MA.