Haskell Data Analysis Cookbook

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.

探索直觀的資料分析技巧和強大的機器學習方法，使用超過130個實用的配方

概述
- 使用Haskell進行資料分析時的實用而簡潔指南
- 從收集到視覺化的每個階段的配方
- 深入示範各種工具、解決方案和技術的例子

詳細內容
本書將帶您穿越資料分析的所有步驟。它提供了Haskell和資料建模之間的協同作用，包括精心選擇的例子，展示了一些最受歡迎的機器學習技術。

您將從如何從各種來源獲取和清理資料開始。然後，您將學習如何使用各種資料結構，如樹和圖。資料分析的核心內容包括統計技術、並行處理、並發處理和機器學習算法，以及各種視覺化和導出結果的例子。通過本書，您將學會最大限度地發揮Haskell在資料分析中的潛力。

本書的學習重點：
- 從文本文件、CSV文件、數據庫和網站等各種來源獲取和分析原始資料
- 在各種資料集上實現實用的樹和圖算法
- 應用移動平均和線性回歸等統計方法來理解模式
- 通過並行和並發代碼來加速和簡化耗時的算法
- 使用一些最受歡迎的機器學習算法在資料中尋找群集
- 通過視覺化或導出資料來管理結果

方法
逐步的配方中充滿了實用的程式碼範例和引人入勝的例子，展示了Haskell的實際應用以及背後的概念。

本書的讀者
本書向功能性開發人員和分析師展示如何利用他們對Haskell的現有知識進行高質量的資料分析。假設讀者對資料集和函數式編程有良好的理解。