也许认识 Haskell 的人并不多，这是一种函数式编程语言（也许明白“函数式编程”概念的人也不是很多）。
总之就是很棒的感觉

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

• 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
  

Google 翻译：

本书将带您了解数据分析中涉及的所有步骤。它提供了Haskell和数据建模之间的协同作用，包括精心挑选的一些最流行的机器学习技术示例。

您将从如何从各种来源获取和清理数据开始。然后，您将学习如何使用各种数据结构，例如树和图。数据分析的主要内容涉及统计技术，并行性，并发性和机器学习算法，以及可视化和导出结果的各种示例。在本书的最后，您将获得使用Haskell进行数据分析时最大限度发挥潜力的技术。

你会学到什么

• 从各种来源获取和分析原始数据，包括文本文件，CSV文件，数据库和网站
• 在各种数据集上实现实用的树和图算法
• 应用统计方法（如移动平均和线性回归）来理解模式
• 使用并行和并发代码来加速和简化耗时的算法
• 使用一些最流行的机器学习算法在数据中查找群集
• 通过可视化或导出数据来管理结果