Haskell Data Analysis Cookbook

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Автор:Nishant Shukla

Название:Haskell Data Analysis Cookbook

Издательство:Packt Publishing - ebooks Account

Год:2014

ISBN:1783286334

Формат:PDF + EPUB + MOBI

Размер:3 MB + 5 MB + 8 MB

Страниц:288

Язык:English

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

Overview

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

Approach

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.