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Description

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

Find clusters in data using some of the most popular machine learning algorithms

Manage results by visualizing or exporting data

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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Amazon verified reviews

Nelson Solano Nov 09, 2017

Was intimidated by all the content within this book, but turns out it's very approachable! Lots of examples and different ways of explaining concepts. I'm already beginning to feel like I have a stronger grasp with Haskell, especially in the context to data science and statistics. I recommend this book to anyone who wants an intro to data analysis techniques for real-world use.

Amazon Verified review

Student May 12, 2015

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.

David Jameson Jul 05, 2014

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.

garrison jensen Apr 03, 2015

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.

Jake McCrary Sep 01, 2014

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.RecommendationI’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.

Haskell Data Analysis cookbook: Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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