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Haskell Data Analysis Cookbook (Anglais) Broché – 25 juin 2014

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Descriptions du produit

Présentation de l'éditeur

Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

About This Book

  • A practical and concise guide to using Haskell when getting to grips with data analysis
  • Recipes for every stage of data analysis, from collection to visualization
  • In-depth examples demonstrating various tools, solutions and techniques

Who This Book Is For

This book shows functional developers and analysts how to leverage their existing knowledge of Haskell specifically for high-quality data analysis. A good understanding of data sets and functional programming is assumed.

What You Will Learn

  • Obtain and analyze raw data from various sources including text files, CSV files, databases, and websites
  • Implement practical tree and graph algorithms on various datasets
  • Apply statistical methods such as moving average and linear regression to understand patterns
  • Fiddle with parallel and concurrent code to speed up and simplify time-consuming algorithms
  • Find clusters in data using some of the most popular machine learning algorithms
  • Manage results by visualizing or exporting data

In Detail

This book will take you on a voyage through all the steps involved in data analysis. It provides synergy between Haskell and data modeling, consisting of carefully chosen examples featuring some of the most popular machine learning techniques.

You will begin with how to obtain and clean data from various sources. You will then learn how to use various data structures such as trees and graphs. The meat of data analysis occurs in the topics involving statistical techniques, parallelism, concurrency, and machine learning algorithms, along with various examples of visualizing and exporting results. By the end of the book, you will be empowered with techniques to maximize your potential when using Haskell for data analysis.

Biographie de l'auteur

Nishant Shukla

Nishant Shukla is a computer scientist with a passion for mathematics. Throughout the years, he has worked for a handful of start-ups and large corporations including WillowTree Apps, Microsoft, Facebook, and Foursquare. Stepping into the world of Haskell was his excuse for better understanding Category Theory at first, but eventually, he found himself immersed in the language. His semester-long introductory Haskell course in the engineering school at the University of Virginia (http://shuklan.com/haskell) has been accessed by individuals from over 154 countries around the world, gathering over 45,000 unique visitors. Besides Haskell, he is a proponent of decentralized Internet and open source software. His academic research in the fields of Machine Learning, Neural Networks, and Computer Vision aim to supply a fundamental contribution to the world of computing.

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6 internautes sur 6 ont trouvé ce commentaire utile 
Great idea, I have been looking for a cookbook ... 6 juillet 2014
Par David Jameson - Publié sur Amazon.com
Format: Format Kindle
Great idea, I have been looking for a cookbook like this for some time and I have been slowly working through the examples. The Haskell world needs books like this really badly as most documentation that you find focuses more on defining the functions rather than helping you use them.

There are some typos here and there such that the compiler produces errors that are hard to understand if you're not already pretty good with Haskell. That had spoiled it a bit for me at first.

However, the great news is that up to date source code is available on github and so as long as you get code from there rather than just copying from the book directly, you should be fine.
1 internautes sur 1 ont trouvé ce commentaire utile 
A well recommended reference for data analysis in Haskell 12 mai 2015
Par Student - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
This book enumerates through dozens of important algorithms used in typical data analysis tasks. It’s one of the most practical and hands-on books on this subject for the Haskell programming language. The examples tie together nicely. I can easily copy and paste the code to test each algorithm. The author also provides the code for each recipe on GitHub.
I would recommend this to anyone who has touched Haskell and is willing to explore more interesting applications.
I like it, I will use it as a reference ... 3 avril 2015
Par garrison jensen - Publié sur Amazon.com
Format: Broché Achat vérifié
I thought this book would explain algorithms. It doesn't. It simply points to numerous libraries that already implement them.
I like it, I will use it as a reference for libraries. But if you are expecting to find advice on implementing algorithms yourself, this is not the book for you.
5 internautes sur 10 ont trouvé ce commentaire utile 
A cookbook format Haskell book. 1 septembre 2014
Par Amazon Customer - Publié sur Amazon.com
Format: Format Kindle
Packt Publishing recently asked me to write a review of the book Haskell Data Analysis Cookbook by Nishant Shukla. The book is broken into small sections that show you how to do a particular task related to data analysis. These tasks vary from reading a csv file or parsing json to listening to a stream of tweets.

I’m not a Haskell programmer. My Haskell experience is limited to reading some books (Learn You a Haskell for Great Good and most of Real World Haskell) and solving some toy problems. All of reading and programming happened years ago though so I’m out of practice.

This book is not for a programmer that is unfamiliar with Haskell. If you’ve never studied it before you’ll find yourself turning towards documentation. If you enter this book with a solid understanding of functional programming you can get by with a smaller understanding of Haskell but you will not get much from the book.

I’ve only read a few cookbook style books and this one followed the usual format. It will be more useful as a quick reference than as something you would read through. It doesn’t dive deep into any topic but does point you toward libraries for various tasks and shows a short example of using them.

A common critic I have of most code examples applies to this book. Most examples do not do qualified imports of namespaces or selective imports of functions from namespaces. This is especially useful when your examples might be read by people who are not be familiar with the languages standard libraries. Reading code and immediately knowing where a function comes from is incredibly useful to understanding.

The code for this book is available on GitHub. It is useful to look at the full example for a section. The examples in the book are broken into parts with English explanations and I found that made it hard to fully understand how the code fit together. Looking at the examples in the GitHub repo helped.


I’d recommend this book for Haskell programmers who find the table of contents interesting. If you read the table of contents and think it would be useful to have a shallow introduction to the topics listed then you’ll find this book useful. It doesn’t give a detailed dive into anything but at least gives you a starting point.

If you either learning Haskell or using Haskell then this book doesn’t have much to offer you.
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