Data Science at the Command Line. 2nd Edition

Spis treści książki

• Foreword
• Preface
  • What to Expect from This Book
  • Changes for the Second Edition
  • How to Read This Book
  • Who This Book Is For
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments for the Second Edition (2021)
  • Acknowledgments for the First Edition (2014)
• 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
      • Based on location
      • Based on a pattern
      • Based on randomness
    • 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
      • Concatenate horizontally
      • Joining
  • 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
  • Summary
  • For Further Exploration
• 11. Conclusion
  • Lets Recap
  • Three Pieces of Advice
    • Be Patient
    • Be Creative
    • Be Practical
  • Where to Go from Here
    • The Command Line
    • Shell Programming
    • Python, R, and SQL
    • APIs
    • Machine Learning
  • Getting in Touch
• A. List of Command-Line Tools
  • alias
  • awk
  • aws
  • bash
  • bat
  • bc
  • body
  • cat
  • cd
  • chmod
  • cols
  • column
  • cowsay
  • cp
  • csv2vw
  • csvcut
  • csvgrep
  • csvjoin
  • csvlook
  • csvquote
  • csvsort
  • csvsql
  • csvstack
  • csvstat
  • curl
  • cut
  • display
  • dseq
  • echo
  • env
  • export
  • fc
  • find
  • fold
  • for
  • fx
  • git
  • grep
  • gron
  • head
  • header
  • history
  • hostname
  • in2csv
  • jq
  • json2csv
  • l
  • less
  • ls
  • make
  • man
  • mkdir
  • mv
  • nano
  • nl
  • parallel
  • paste
  • pbc
  • pip
  • pup
  • pwd
  • python
  • R
  • rev
  • rm
  • rush
  • sample
  • scp
  • sed
  • seq
  • servewd
  • shuf
  • skll
  • sort
  • split
  • sponge
  • sql2csv
  • ssh
  • sudo
  • tail
  • tapkee
  • tar
  • tee
  • telnet
  • tldr
  • tr
  • tree
  • trim
  • ts
  • type
  • uniq
  • unpack
  • unrar
  • unzip
  • vw
  • wc
  • which
  • xml2json
  • xmlstarlet
  • xsv
  • zcat
  • zsh
• Index

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