• Title Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools
• Author(s) Jeroen Janssens
• Publisher: O'Reilly Media; 2nd edition (September 7, 2021); eBook (Creative Commons Licensed)
• License(s): CC BY-ND 4.0
• Paperback: 282 pages
• eBook: HTML
• Language: English
• ISBN-10: 1492087912
• ISBN-13: 978-1492087915
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Book Description

This hands-on 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 100 Unix power 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, engineers, system administrators, and researchers.

• Obtain data from websites, APIs, databases, and spreadsheets
• Perform scrub operations on plain text, CSV, HTML/XML, and JSON
• Explore data, compute descriptive statistics, and create visualizations
• Manage your data science workflow using Drake
• Create reusable tools from one-liners and existing Python or R code
• Parallelize and distribute data-intensive pipelines using GNU Parallel
• Model data with dimensionality reduction, clustering, regression, and classification algorithms

• Jeroen Janssens teaches data science; often through training and coaching, occasionally through speaking, and infrequently through writing. Currently, Jeroen is the CEO of Data Science Workshops, which organises open enrollment workshops, in-company courses, inspiration sessions, hackathons, and meetups.

• Amazon

• Data Science
• Unix/Linux Shell Scripting
• Data Analysis and Data Mining
• Big Data
• Machine Learning

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