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 text, CSV, HTML, XML, and JSON files
Explore data, compute descriptive statistics, and create visualizations
Manage your data science workflow
Create your own tools from one-liners and existing Python or R code
Parallelize and distribute data-intensive pipelines
Model data with dimensionality reduction, regression, and classification algorithms
Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark

1. Introduction

Data Science Is OSEMN
Obtaining Data
Scrubbing Data
Exploring Data
Modeling Data
Interpreting Data
Intermezzo Chapters
What Is the Command Line?
Why Data Science at the Command Line?
The Command Line Is Agile
The Command Line Is Augmenting
The Command Line Is Scalable
The Command Line Is Extensible
The Command Line Is Ubiquitous
Summary
For Further Exploration

2. Getting Started

Getting the Data
Installing the Docker Image
Essential Unix Concepts
The Environment
Executing a Command-Line Tool
Five Types of Command-Line Tools
Combining Command-Line Tools
Redirecting Input and Output
Working with Files and Directories
Managing Output
Help!
Summary
For Further Exploration

3. Obtaining Data

Overview
Copying Local Files to the Docker Container
Downloading from the Internet
Introducing curl
Saving
Other Protocols
Following Redirects
Decompressing Files
Converting Microsoft Excel Spreadsheets to CSV
Querying Relational Databases
Calling Web APIs
Authentication
Streaming APIs
Summary
For Further Exploration

4. Creating Command-Line Tools

Overview
Converting One-Liners into Shell Scripts
Step 1: Create a File
Step 2: Give Permission to Execute
Step 3: Define a Shebang
Step 4: Remove the Fixed Input
Step 5: Add Arguments
Step 6: Extend Your PATH
Creating Command-Line Tools with Python and R
Porting the Shell Script
Processing Streaming Data from Standard Input
Summary
For Further Exploration

5. Scrubbing Data

Overview
Transformations, Transformations Everywhere
Plain Text
Filtering Lines
Extracting Values
Replacing and Deleting Values
CSV
Bodies and Headers and Columns, Oh My!
Performing SQL Queries on CSV
Extracting and Reordering Columns
Filtering Rows
Merging Columns
Combining Multiple CSV Files
Working with XML/HTML and JSON
Summary
For Further Exploration

6. Project Management with Make

Overview
Introducing Make
Running Tasks
Building, for Real
Adding Dependencies
Summary
For Further Exploration

7. Exploring Data

Overview
Inspecting Data and Its Properties
Header or Not, Here I Come
Inspect All the Data
Feature Names and Data Types
Unique Identifiers, Continuous Variables, and Factors
Computing Descriptive Statistics
Column Statistics
R One-Liners on the Shell
Creating Visualizations
Displaying Images from the Command Line
Plotting in a Rush
Creating Bar Charts
Creating Histograms
Creating Density Plots
Happy Little Accidents
Creating Scatter Plots
Creating Trend Lines
Creating Box Plots
Adding Labels
Going Beyond Basic Plots
Summary
For Further Exploration

8. Parallel Pipelines

Overview
Serial Processing
Looping Over Numbers
Looping Over Lines
Looping Over Files
Parallel Processing
Introducing GNU Parallel
Specifying Input
Controlling the Number of Concurrent Jobs
Logging and Output
Creating Parallel Tools
Distributed Processing
Get List of Running AWS EC2 Instances
Running Commands on Remote Machines
Distributing Local Data Among Remote Machines
Processing Files on Remote Machines
Summary
For Further Exploration

9. Modeling Data

Overview
More Wine, Please!
Dimensionality Reduction with Tapkee
Introducing Tapkee
Linear and Nonlinear Mappings
Regression with Vowpal Wabbit
Preparing the Data
Training the Model
Testing the Model
Classification with SciKit-Learn Laboratory
Preparing the Data
Running the Experiment
Parsing the Results
Summary
For Further Exploration

10. Polyglot Data Science

Overview
Jupyter
Python
R
RStudio
Apache Spark

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.