- Paperback: 227 pages
- Publisher: Packt Publishing - ebooks Account; 2 edition (January 22, 2015)
- Language: English
- ISBN-10: 1784390291
- ISBN-13: 978-1784390297
- Product Dimensions: 7.5 x 0.8 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #3,181,422 in Books (See Top 100 in Books)
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Clojure Data Analysis Cookbook- Second Edition 2nd Edition
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About the Author
Eric Rochester enjoys reading, writing, and spending time with his wife and kids. When he's not doing these things, he programs in a variety of languages and platforms, including websites and systems in Python, and libraries for linguistics and statistics in C#. Currently, he is exploring functional programming languages, including Clojure and Haskell. He works at Scholars' Lab in the library at the University of Virginia, helping humanities professors and graduate students realize their digitally informed research agendas. He is also the author of Mastering Clojure Data Analysis, Packt Publishing.
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Top customer reviews
You should have some knowledge about Clojure, especially how to interact with the repl, load & create projects.
I have taken giant steps in my own projects thanks to this book. I have been too lazy to dig into all the great libraries that Clojure has to offer but now all that has changed.
Clojure is a great tool for data analysis!
Can Arel, Stockholm
Rochester. Format ebook PDF
Two years after the first version Eric Rochester has published an
updated version of his book "Clojure Data Analysis Cookbook".
The book gives a nice overview of data analysis in the clojure
programming language. It provides hundreds of useful tips on various
software such as Incanter: the clojure statistics platform or Weka: a
java platform for machine learning.
The examples provided are easy to test assuming you have a basic
knowledge of clojure (especially regarding the repl interaction). As
most examples are independent from each other it is easy to pick
recipes without having to follow the whole chapter from the beginning.
The first two chapter deal with the importation and the validation of
data using common format such as XML,JSON,CSV or RDF. It appears to be
very useful as it is a mandatory step (usually the first) of data
analysis. While it is not very complicated to do everything by
yourself those tips may save you some time.
Clojure has a very good concurrency and parallel model by default,
which are usually covered in every clojure introduction book but you will still
find information in this book that you don't find in
others. I particularly like the Monte Carlo and simulated annealing methods
that find optimal partition size for parallel processing.
Chapter 8 is perhaps less interresting becausse it covers Clojure
interaction with Mathematica and the R language. and the only reason to
use them is when a big library or framework is not available in
clojure and you have some constraints (e.g. time or performance) that prevent
you from implementing it in clojure.
As the title implies the book will not make you autonomous on data
analysis but it would be good to have some tips and examples on how to
design and build a full scale data analysis oriented application.
A good book for discovering and playing with data in Clojure.
The author goes through the different phases of Data Analysis, starting from the low level details about how to read data from actual sources, continuing on how to clean it up to obtain meaningful results and presenting different algorithms for actually performing data analysis.
The recipes go from querying, aggregating data and displaying, to statistical analysis and machine learning (clustering and classification).
Recipes are presented in a very clear way, and they give the reader a clear context where to apply them, how they work, actual working code to experiment with and some additional reference for getting more in-depth information.
I really appreciated the fact the author is well versed both in the theorical aspect and in Clojure programming. He presents very important details about advanced topics like parallelism, concurrency and laziness, and warn the reader about the pitfalls to be aware of. For example when talking about lazy-data-reading he clearly explains how to correcly handle underlying resources explicitly showing the source of potential issues.
I read also the first edition and I've found that almost all the common recipes have been updated, and also a new chapter about unstructured and textual data has been added.
Even though I am not a data analyst this book was very clear, and gave me a lot of insights about how to deal with data. I will for sure apply some of these recipes in my daily work to make more sense of what happens in what I manage.
Most recent customer reviews
Most books will show you "How to read data" and go through a trivial parse string sample.Read more