Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
11
Index

Chapter 1. Statistics

 

"The people who cast the votes decide nothing. The people who count the votes decide everything."

 
 --Joseph Stalin

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.