Haskell Data Analysis Cookbook
By Nishant Shukla
Publisher: Packt Publishing
Final Release Date: June 2014
Pages: 334

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.


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 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.

Product Details
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oreillyHaskell Data Analysis Cookbook

(based on 3 reviews)

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The Recipes are a Goldmine

By John

from Los Angeles, CA

About Me Developer


  • Comprehensive
  • Easy to understand
  • Helpful examples


  • Typos

Best Uses

  • Intermediate

Comments about oreilly Haskell Data Analysis Cookbook:

I bought this book as a reference for using Haskell at work. There's not much out there for data analysis and machine learning in Haskell so this book was my one last hope. Lucky for me, the recipes included in the book cover the entire data analysis spectrum! With the code provided online, I had exactly the reference I needed to start building my own analysis platform.

The greatest part of the entire book is that each recipe is independent of another. I can jump into the book at any point and review a recipe, just like an encyclopedia. Fortunately the code is laid out on the author's GitHub so I don't need to copy and paste anything over. It all just works.

Probably the single most significant downfall of the book are the typos I run into once in a while. They're easy to decipher, but still surprised to see them once in a while.


Data Analysis with the power of Haskell

By Ivan Fraixedes

from London, UK

About Me Developer


  • Hands On Lab
  • Helpful examples


    Best Uses

    • Intermediate

    Comments about oreilly Haskell Data Analysis Cookbook:

    Haskell is one of the languages which I have aways been curious, however it is one of them which is not spread in the professional world, moreover nobody want to explore new horizons for different reasons outside of this review.

    I could make an introduction to it in so many ways, but I've never got the time to do it, because other duties and interests took priority.

    When I was requested to review this book, I considered a good opportunity to get an introduction to Haskell, moreover how to get into it in a practical way than an academic one.

    This great book allowed me to discover the "odd" Haskell's syntax but its strengths and its powerful parallel and concurrent computation as well.

    Because the book is centred in how to use Haskell for data analysis, I got the chance to see how this language can be used for usefulness; today we live in a world where the data is growing so much faster that so many people can imagine, but people who work with this amount of raw data directly or just creating systems which have to support it we are constantly looking different ways, approaches and new technologies which may drive to enhance and improve our systems in several aspects.

    This awesome book drive into the core of Haskell and its API and available libraries to analyse data, starting with getting into from different sources as JSON files, databases as MongoDB, sanitise for thereafter orchestrate with common and appreciated data structures.

    When our data is into the those data structures, then it teaches about its analysing with statistics and its common techniques, then boosting up the performance with the awesome parallel and concurrent Haskell design.

    However, the book does not stop here, it follows to the next step which the most of us developers may have fun time, it jumps into the Real-Time Data show how to analyse data coming from Twitter, IRC channels, polling web servers, watching file system, communicating with sockets and why not getting the data from a camera and tinker with it.

    Furthermore, it will teach you to do a nice outcome with all the efforts done to get the data into your code and processed it, as visualising it with some plotting libraries and ending up the pipeline teaching you how to export it to several formats to keep it and reporting it to people who make decisions from it.

    In this moment you have to feel that it is interesting enough to see a practical case of Haskell and get it to have in your hands and move your ideas forward, so go for it on PacktPub and have a great read.

    (0 of 1 customers found this review helpful)


    Haskell Data Analysis

    By Jake

    from Chicago, IL

    Verified Reviewer

    Comments about oreilly Haskell Data Analysis Cookbook:

    [Disclosure: I was given this book to review]

    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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