This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 80 tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, and engineers; software and machine learning engineers; and system administrators. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTM, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create reusable command-line tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms

这本经过彻底修订的指南展示了命令行的灵活性如何帮助你成为一个更有效率和更有成效的数据科学家。你将学习如何结合小而强大的命令行工具来快速获取、擦洗、探索和建模你的数据。为了让你开始学习，作者Jeroen Janssens提供了一个包含80多个工具的Docker镜像--无论你在Windows、macOS还是Linux上工作，都是有用的。你会很快发现为什么命令行是一种敏捷的、可扩展的和可延伸的技术。即使你对用Python或R处理数据感到满意，你也会学到如何通过利用命令行的力量来大大改善你的数据科学工作流程。本书是数据科学家、分析师和工程师；软件和机器学习工程师；以及系统管理员的理想选择。从网站、API、数据库和电子表格中获取数据 对文本、CSV、HTM、XML和JSON文件进行刷新操作 探索数据，计算描述性统计，并创建可视化 管理你的数据科学工作流程 从单行代码和现有的Python或R代码中创建可重复使用的命令行工具 平行化和分发数据密集型管道 用降维、聚类、回归和分类算法对数据建模