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Clojure for Data Science

Clojure for Data Science

By : Garner
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Clojure for Data Science

Clojure for Data Science

5 (4)
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)
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11
Index

Downloading the data

The dataset for this chapter has been made available by the Complex Systems Research Group at the Medical University of Vienna. The analysis we'll be performing closely mirrors their research to determine the signals of systematic election fraud in the national elections of countries around the world.

Note

For more information about the research, and for links to download other datasets, visit the book's wiki or the research group's website at http://www.complex-systems.meduniwien.ac.at/elections/election.html.

Throughout this book we'll be making use of numerous datasets. Where possible, we've included the data with the example code. Where this hasn't been possible—either because of the size of the data or due to licensing constraints—we've included a script to download the data instead.

Chapter 1, Statistics is just such a chapter. If you've cloned the chapter's code and intend to follow the examples, download the data now by executing the following on the command line from within the project's directory:

script/download-data.sh

The script will download and decompress the sample data into the project's data directory.

Tip

If you have any difficulty running the download script or would like to follow manual instructions instead, visit the book's wiki at http://wiki.clojuredatascience.com for assistance.

We'll begin investigating the data in the next section.

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