Chapter 1, Statistics, introduces Incanter, Clojure's primary statistical computing library used throughout the book. With reference to the data from the elections in the United Kingdom and Russia, we demonstrate the use of summary statistics and the value of statistical distributions while showing a variety of comparative visualizations.

Chapter 2, Inference, covers the difference between samples and populations, and statistics and parameters. We introduce hypothesis testing as a formal means of determining whether the differences are significant in the context of A / B testing website designs. We also cover sample bias, effect size, and solutions to the problem of multiple testing.

Chapter 3, Correlation, shows how we can discover linear relationships between variables and use the relationship to make predictions about some variables given others. We implement linear regression—a machine learning algorithm—to predict the weights of Olympic swimmers given their heights, using only core Clojure functions. We then make our model more sophisticated using matrices and more data to improve its accuracy.

Chapter 4, Classification, describes how to implement several different types of machine learning algorithm (logistic regression, naive Bayes, C4.5, and random forests) to make predictions about the survival rates of passengers on the Titanic. We learn about another test for statistical significance that works for categories instead of continuous values, explain various issues you're likely to encounter while training machine learning models such as bias and overfitting, and demonstrate how to use the clj-ml machine learning library.

Chapter 5, Big Data, shows how Clojure can leverage the parallel capabilities in computers of all sizes using the reducers library, and how to scale up these techniques to clusters of machines on Hadoop with Tesser and Parkour. Using ZIP code level tax data from the IRS, we demonstrate how to perform statistical analysis and machine learning in a scalable way.

Chapter 6, Clustering, shows how to identify text documents that share similar subject matter using Hadoop and the Java machine learning library, Mahout. We describe a variety of techniques particular to text processing as well as more general concepts related to clustering. We also introduce some more advanced features of Parkour that can help get the best performance from your Hadoop jobs.

Chapter 7, Recommender Systems, covers a variety of different approaches to the challenge of recommendation. In addition to implementing a recommender with core Clojure functions, we tackle the ancillary challenge of dimensionality reduction by using principle component analysis and singular value decomposition, as well as probabilistic set compression using Bloom filters and the MinHash algorithm. Finally, we introduce the Sparkling and MLlib libraries for machine learning on the Spark distributed computation framework and use them to produce movie recommendations with alternating least squares.

Chapter 8, Network Analysis, shows a variety of ways of analyzing graph-structured data. We demonstrate the methods of traversal using the Loom library and then show how to use the Glittering and GraphX libraries with Spark to discover communities and influencers in social networks.

Chapter 9, Time Series, demonstrates how to fit curves to simple time series data. Using data on the monthly airline passenger counts, we show how to forecast future values for more complex series by training an autoregressive moving-average model. We do this by implementing a method of parameter optimization called maximum likelihood estimation with help from the Apache Commons Math library.

Chapter 10, Visualization, shows how the Clojure library Quil can be used to create custom visualizations for charts not provided by Incanter, and attractive graphics that can communicate findings clearly to your audience, whatever their background.

The code for each chapter has been made available as a project on GitHub at https://github.com/clojuredatascience. The example code can be downloaded as a zip file from there, or cloned with the Git command-line tool. All of the book's examples can be compiled and run with the Leiningen build tool as described in Chapter 1, Statistics.

This book assumes that you're already able to compile and run Clojure code using Leiningen (http://leiningen.org/). Refer to Leiningen's website if you're not yet set up to do this.

In addition, the code for many of the sample chapters makes use of external datasets. Where possible, these have been included together with the sample code. Where this has not been possible, instructions for downloading the data have been provided in the sample code's README file. Bash scripts have also been provided with the relevant sample code to automate this process. These can be run directly by Linux and OS X users, as described in the relevant chapter, provided the curl, wget, tar, gzip, and unzip utilities are installed. Windows users may have to install a Linux emulator such as Cygwin (https://www.cygwin.com/) to run the scripts.

This book is intended for intermediate and advanced Clojure programmers who want to build their statistical knowledge, apply machine learning algorithms, or process large amounts of data with Hadoop and Spark. Many aspiring data scientists will benefit from learning all of these skills, and Clojure for Data Science is intended to be read in order from the beginning to the end. Readers who approach the book in this way will find that each chapter builds on concepts introduced in the prior chapters.

If you're not already comfortable reading Clojure code, you're likely to find this book particularly challenging. Fortunately, there are now many excellent resources for learning Clojure and I do not attempt to replicate their work here. At the time of writing, Clojure for the Brave and True (http://www.braveclojure.com/) is a fantastic free resource for learning the language. Consult http://clojure.org/getting_started for links to many other books and online tutorials suitable for newcomers.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Each example is a function in the cljds.ch1.examples namespace that can be run."

A block of code is set as follows:

(defmulti load-data identity)

(defmethod load-data :uk [_]
  (-> (io/resource "UK2010.xls")
      (str)
      (xls/read-xls)))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

    (q/fill (fill-fn x y))
    (q/rect x-pos y-pos x-scale y-scale))
    (q/save "heatmap.png"))]
    (q/sketch :setup setup :size size))

Any command-line input or output is written as follows:

lein run –e 1.1

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Each time the New Sample button is pressed, a pair of new samples from an exponential distribution with population means taken from the sliders are generated."

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