Over the course of the following ten chapters of Clojure for Data Science, we'll attempt to discover a broadly linear path through the field of data science. In fact, we'll find as we go that the path is not quite so linear, and the attentive reader ought to notice many recurring themes along the way.

Descriptive statistics concern themselves with summarizing sequences of numbers and they'll appear, to some extent, in every chapter in this book. In this chapter, we'll build foundations for what's to come by implementing functions to calculate the mean, median, variance, and standard deviation of numerical sequences in Clojure. While doing so, we'll attempt to take the fear out of interpreting mathematical formulae.

As soon as we have more than one number to analyze it becomes meaningful to ask how those numbers are distributed. You've probably already heard expressions such as "long tail" and the "80/20 rule". They concern the spread of numbers throughout a range. We demonstrate the value of distributions in this chapter and introduce the most useful of them all: the normal distribution.

The study of distributions is aided immensely by visualization, and for this we'll use the Clojure library Incanter. We'll show how Incanter can be used to load, transform, and visualize real data. We'll compare the results of two national elections—the 2010 United Kingdom general election and the 2011 Russian presidential election—and see how even basic analysis can provide evidence of potentially fraudulent activity.