Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools, 2nd Edition (Early Release)КНИГИ » ПРОГРАММИНГ
Название: Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools, 2nd Edition (Early Release) Автор: Jeroen Janssens Издательство: O’Reilly Media Год: 2020-09-11 Страниц: 80 Язык: английский Формат: pdf, rtf, epub Размер: 11.4 MB
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.
Today, data scientists can choose from an overwhelming collection of exciting technologies and programming languages. Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples. You may already have experience in one or more of these. If so, then why should you still care about the command line for doing data science? What does the command line have to offer that these other technologies and programming languages do not?
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
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