Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools

by Jeroen Janssens

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 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.

Genres: Computer Science, Programming

280 pages, Paperback

Published September 21, 2021

About the author

Jeroen Janssens, PhD, is a Senior Developer Relations Engineer at Posit, PBC. His expertise lies in visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. He’s passionate about open source and sharing knowledge. He’s the author of Python Polars: The Definitive Guide (O’Reilly, 2025) and Data Science at the Command Line (O’Reilly, 2021). Jeroen holds a PhD in machine learning from Tilburg University and an MSc in artificial intelligence from Maastricht University. He lives with his wife and two kids in Rotterdam, the Netherlands.

Reviews

[Thomas Trenner · Rating 4 out of 5]

This is a really good book, if one is already familiar with the command line and data science. It is definitely not a resource for beginners. It is written in an engaging style, and I picked up a number of helpful tools and tricks. My main points of criticism would be that there are no exercises to test one's understanding of the tools/techniques that were introduced in the chapter and that it (too often for my liking) uses some pretty sophisticated wrapper scripts by the author himself, which obscure things more than they help. I would have preferred learning how to get along with just "standard" tools that don't require an installation of some Go or R runtime on top of some github cloning and building.

[Nigel Thornton · Rating 4 out of 5]

An interesting approach to quick and dirty data science. Excellent!

[. · Rating 1 out of 5]

The methods demonstrated in this book are far far from optimal.
Look for a proper data science book or course preferably in Python or Julia.

[Eric · Rating 4 out of 5]

Excellent overview of how to use command line tools to do common data science tasks. It’s always helpful to learn new ways to do the job more efficiently