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Mastering Clojure Data Analysis
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.
This book is great for those who have experience with Clojure and who 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.
This book is great for those who have experience with Clojure and who 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.
340 pages, Paperback
First published January 1, 2014
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21 people want to read
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Displaying 1 of 1 review
July 4, 2014
I am an experienced programmer in Java, with some experience in Clojure, and I am interested but not an expert in Data Analysis. I have Eric’s previous book - Clojure Data Analysis Cookbook. Although the names of that book and this book (Mastering Clojure Data Analysis) look similar, they cover different aspects. The former talks more about data analysis tools and libraries in Clojure, like Incanter, Cascalog.
Yet this book tries to examine case studies and go into more depth from the perspective of data analysis. It doesn’t focus on the technologies that the implementation uses, instead, it walks the reader through many common areas in data analysis, such as (social) network analysis and topic modeling, which in themselves are interesting topics regardless of whether you use Clojure or not to tackle the problem. Having said that, the author also covers important aspects of related libraries and tools well, including the MALLET machine learning library, d3 for data visualization.
I like this book as it demonstrates interesting case studies in the data analysis world, and how these problems can be solved using Clojure (backed by libraries/tools which may not be written in Clojure but in Java) in a concise and elegant way.
All in all, since the nature of this book is to talk about data analysis using Clojure, it is neither a book about Clojure programming nor a book on the algorithmic aspects of data analysis or how Bayesian works in detail..
(from j.mp/McDla you can find official sample chapters)
Yet this book tries to examine case studies and go into more depth from the perspective of data analysis. It doesn’t focus on the technologies that the implementation uses, instead, it walks the reader through many common areas in data analysis, such as (social) network analysis and topic modeling, which in themselves are interesting topics regardless of whether you use Clojure or not to tackle the problem. Having said that, the author also covers important aspects of related libraries and tools well, including the MALLET machine learning library, d3 for data visualization.
I like this book as it demonstrates interesting case studies in the data analysis world, and how these problems can be solved using Clojure (backed by libraries/tools which may not be written in Clojure but in Java) in a concise and elegant way.
All in all, since the nature of this book is to talk about data analysis using Clojure, it is neither a book about Clojure programming nor a book on the algorithmic aspects of data analysis or how Bayesian works in detail..
(from j.mp/McDla you can find official sample chapters)
Displaying 1 of 1 review