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Haskell Data Analysis Cookbook [Anglais] [Broché]

Nishant Shukla

Prix : EUR 43,86 Livraison à EUR 0,01 En savoir plus.
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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


  • 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

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.

What you will learn from this book

  • 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


Step-by-step recipes filled with practical code samples and engaging examples demonstrate Haskell in practice, and then the concepts behind the code.

Who this book is written 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.

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.

Détails sur le produit

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Amazon.com: 5.0 étoiles sur 5  1 commentaire
4 internautes sur 4 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 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.
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