• Title Data Science at the Command Line: Facing the Future with Time-Tested Tools
• Author(s) Jeroen Janssens
• Publisher: O'Reilly Media, 1 edition (October 12, 2014); eBook (June 10, 2019. Updated continuously)
• License(s): CC BY-ND 4.0
• Paperback 212 pages
• eBook HTML
• Language: English
• ISBN-10: 1491947853
• ISBN-13: 978-1491947852
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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—whether you're on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.

Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.

• 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 is an assistant professor of data science at Tilburg University. As an independent consultant and trainer, he helps organizations making sense of their data. Previously, he was a data scientist at Elsevier in Amsterdam and startups YPlan and Visual Revenue in New York City. Jeroen holds an MSc in artificial intelligence from Maastricht University and a PhD in machine learning from Tilburg University. He's passionate about building open source tools for data science.

• Amazon

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