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Sub‐ sequently, we explain the essential command-line concepts through examples. By the end of this chapter, you’ll have everything you need in order to continue with the first step of doing data science, namely obtaining data. Overview In this chapter, you’ll learn: • How to set up the Data Science Toolbox • Essential concepts and tools necessary to do data science at the command line Setting Up Your Data Science Toolbox In this book we use many different command-line tools. The distribution of GNU/ Linux that we are using, Ubuntu, comes with a whole bunch of command-line tools pre-installed.

In Chapter 9, for example, we’ll be using the command-line tool bigmler that connects to BigML’s pre‐ diction API. Further Reading • Molinaro, A. (2005). SQL Cookbook. O’Reilly Media. • Wikipedia. (2014). List of HTTP status codes. org/wiki/List_of_HTTP_status_codes. Further Reading | 39 CHAPTER 4 Creating Reusable Command-Line Tools Throughout the book, we use a lot of commands and pipelines that basically fit on one line (let’s call those one-liners). Being able to perform complex tasks with just a one-liner is what makes the command line powerful.

The command-line tool pwd (Meyering, 2012) prints the name of the directory where you currently are. By default, when you log in, this is your home directory. You can view the contents of this directory with ls (Stallman & MacKenzie, 2012): $ ls book The command-line tool cd, which is a Bash builtin, allows you to navigate to a differ‐ ent directory: $ cd book/ch02/ $ cd data $ pwd /home/vagrant/book/ch02/data $ cd .. $ pwd /home/vagrant/book/ch02/ The part after cd specifies to which directory you want to navigate to.

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Ambiguous Oracle by Spider Robinson


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