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This is a review of exploratory data analysis, which is the next course in the course sequence. Exploratory data analysis course will cover the principles of how do you create analytic graphics, graphics that allow you to analyze data. It'll talk about exploratory graphs, how you explore the data and get in, create enough graphs to sort of figure out the structure of what's going on. It will talk about the plotting systems in R. So it'l talk about base plotting, lattice plotting and then ggplot2 which is a popular newer platform. It'll talk about hierarchical clustering, K-means clustering and a little about dimension reduction. These are all techniques that you can use to get in and explore data as a first pass. So, for example, it will talk about how to use ggplot2 package to make plots like this, sort of, sort of pretty smooth scatter plots that allow you to like understand the relationship between different variables. It will talk about the principles of analytic graphics, so what are the principles that you need to create graphics that'll be useful sort of to figure out what's actually going on with the data you'll be working with. And then it'll cover things like K-means clustering. So the idea of how do we, if we have a bunch of observations of collecting data on that, how we cluster them into relative groups that are similar to each other as a method of exploring the data and figuring out the structure of what's going on. So this is just a couple of the ideas that will be covered in exploratory data analysis.

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