Mastering Clojure Data Analysis
By Eric Rochester
Publisher: Packt Publishing
Final Release Date: May 2014
Pages: 340

In Detail

Clojure is a Lisp dialect built on top of the Java Virtual Machine. As data increasingly invades more and more parts of our lives, we continually need more tools to deal with it effectively. Data can be organized effectively using Clojure data tools.

Mastering Clojure Data Analysis teaches you how to analyze and visualize complex datasets. With this book, you'll learn how to perform data analysis using established scientific methods with the modern, powerful Clojure programming language with the help of exciting examples drawn from real-world data. This will help you get to grips with advanced topics such as network analysis, the characteristics of social networks, applying topic modeling to get a handle on unstructured textual data, and GIS analysis to apply geospatial techniques to your data analysis problems.

With this guide, you'll learn how to leverage the power and flexibility of Clojure to dig into your data and access the insights it hides.


This book consists of a practical, example-oriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently.

Who this book is for

This book is great for those who have experience with Clojure and need to use it to perform data analysis. This book will also be hugely beneficial for readers with basic experience in data analysis and statistics.

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oreillyMastering Clojure Data Analysis

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(2 of 3 customers found this review helpful)


good book on data analysis using clojure

By David

from Atlanta, GA

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  • Easy to understand
  • Helpful examples


    Best Uses

    • Intermediate

    Comments about oreilly Mastering Clojure Data Analysis:

    First off my disclaimer I volunteered to review 'Mastering Clojure Data Analysis' - . I did not receive any compensation outside of an electronic copy of the book. I volunteered because of my interests in both Clojure and data analysis. The book is structured in a series of independent chapters modeled like use cases. He does a very good job of covering the problem space and why the algorithm makes sense to use. I would say that the orders of the words in the title would be more appropriately 'mastering data analysis using clojure' as clojure isn't the focus nor should it be. Since the chapters are independent that technology stacks are slightly different – i.e. incanter vs d3js, weka vs mallet etc. There isn't 100% overlap in those libraries but there is definitely overlap. Also since it made sense to use these libraries the clojure code is really just java interop over them which is the correct and pragmatic choice. That is why I would change the ordering of the title. Overall I would rate this book 3.5/5.

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