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Haskell Data Analysis Cookbook

Nishant Shukla · 1 HN comments
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Amazon Summary
Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipesAbout This BookA practical and concise guide to using Haskell when getting to grips with data analysisRecipes for every stage of data analysis, from collection to visualizationIn-depth examples demonstrating various tools, solutions and techniquesWho This Book Is ForThis 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 LearnObtain and analyze raw data from various sources including text files, CSV files, databases, and websitesImplement practical tree and graph algorithms on various datasetsApply statistical methods such as moving average and linear regression to understand patternsFiddle with parallel and concurrent code to speed up and simplify time-consuming algorithmsFind clusters in data using some of the most popular machine learning algorithmsManage results by visualizing or exporting dataIn DetailThis 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.
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Nov 17, 2014 · qubitcoder on How I Start: Haskell
For more practical examples like this, I highly recommend the Haskell Data Analysis Cookbook [1].

After learning the basics of Haskell, having a book chock full of practical examples of things like handling CVS files, JSON, trees, graphs, machine learning, and Haskell's outstanding support for parallel computation--is a really helpful resource. The code is extremely well written; clear, concise, and readable. The author has also put the source code on GitHub for each chapter. [2]

Afterwards, to take your skills to the next level, check out Simon Marlow's Parallel and Concurrent Programming in Haskell [3]. Simon now works at Facebook, where they're using Haskell in production [4] [5].

[1] http://www.amazon.com/Haskell-Analysis-Cookbook-Nishant-Shuk...

[2] https://github.com/BinRoot/Haskell-Data-Analysis-Cookbook

[3] http://www.amazon.com/Parallel-Concurrent-Programming-Haskel...

[4] https://code.facebook.com/posts/302060973291128/open-sourcin...

[5] http://community.haskell.org/~simonmar/papers/haxl-icfp14.pd...

houshuang
Yes, I found the Data Analysis cookbook very helpful as well. However, it really drove home to me the need for a dataframe/Pandas-like library in Haskell... About half the code in every one of his examples was reading a CSV file, parsing it into some ad-hoc data structure etc. For very robust production apps, that can be justifiable, but for just a quick look at some data, it should just be a one-liner...
tel
This rings very true to my use as well. I'd like to see something like Vinyl specialized to data frames, but nobody has built it yet.

Edit: see the post by coolsunglasses below—Carter's work is certainly the furthest along in this direction that I'm aware of.

carterschonwald
And wrt syntx sugar for hlists and vinyl, there's some somestuff I hope to get into the merge window for ghc this week that might make that much nicer to work with. I hope.
tel
Yay!
carterschonwald
well,try out the hcons/hnil stuff in https://github.com/cartazio/HetList/blob/master/HetList.hs and give me feedback asap so I can somehow wing getting it under consideration later this week. merge window for big stuff is FRIDAY
coolsunglasses
Carter is working on a numerical stack for Haskell that should make writing a pandas-esque library in Haskell pretty easy.

He can be found at https://twitter.com/cartazio and in the #numerical-haskell Freenode IRC channel.

carterschonwald
of course that ignoring the fact that I kinda think people say "pandas" as a proxy for "i want a decent multi dimensional aggregation db thats a library for my language"... kinda thing. People say "data frame" but can mean a HUGE range of actual workloads
coolsunglasses
https://github.com/sdiehl/frame and https://github.com/ekmett/tables came up in IRC as well.
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