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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.This book is about doing data science at the command line.These topics are applicable to any step in the data science process.In Chapter 4, we discuss how to create reusable tools for the command line.These personal tools can come from both long commands that you have typed on the command line, or from existing code that you have written in, say, Python or R.Being able to create your own tools allows you to become more efficient and productive.Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples.

Second, we’ll list five important advantages of the command line.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.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? This first chapter will answer these questions as follows.