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Data Science at the Command Line
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2.4 Further Reading

 来源:Jeroen Janssens 浏览 571 扫码 分享 2019-08-10 10:05:25
  • 2.4 Further Reading

    2.4 Further Reading

    • Heddings, Lowell. 2006. “Keyboard Shortcuts for Bash.” http://www.howtogeek.com/howto/ubuntu/keyboard-shortcuts-for-bash-command-shell-for-ubuntu-debian-suse-redhat-linux-etc.
    • Peek, Jerry, Shelley Powers, Tim O’Reilly, and Mike Loukides. 2002. Unix Power Tools. 3rd Ed. O’Reilly Media.
    当前内容版权归 Jeroen Janssens 或其关联方所有，如需对内容或内容相关联开源项目进行关注与资助，请访问 Jeroen Janssens .
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    • Welcome
    • Preface
    • Chapter 1 Introduction
      • 1.1 Overview
      • 1.2 Data Science is OSEMN
      • 1.3 Intermezzo Chapters
      • 1.4 What is the Command Line?
      • 1.5 Why Data Science at the Command Line?
      • 1.6 A Real-world Use Case
      • 1.7 Further Reading
    • Chapter 2 Getting Started
      • 2.1 Overview
      • 2.2 Installing the Docker Image
      • 2.3 Essential GNU/Linux Concepts
      • 2.4 Further Reading
    • Chapter 3 Obtaining Data
      • 3.1 Overview
      • 3.2 Copying Local Files to the Data Science Toolbo
      • 3.3 Decompressing Files
      • 3.4 Converting Microsoft Excel Spreadsheets
      • 3.5 Querying Relational Databases
      • 3.6 Downloading from the Internet
      • 3.7 Calling a Web API
      • 3.8 Further Reading
    • Chapter 4 Creating Reusable Command-line Tools
      • 4.1 Overview
      • 4.2 Converting One-liners into Shell Scripts
      • 4.3 Creating Command-line Tools with Python and R
      • 4.4 Further Reading
    • Chapter 5 Scrubbing Data
      • 5.1 Overview
      • 5.2 Common Scrub Operations for Plain Text
      • 5.3 Working with CSV
      • 5.4 Working with XML/HTML and JSON
      • 5.5 Common Scrub Operations for CSV
      • 5.6 Further Reading
    • Chapter 6 Managing Your Data Workflow
      • 6.1 Overview
      • 6.2 Introducing Drake
      • 6.3 Installing Drake
      • 6.4 Obtain Top E-books from Project Gutenberg
      • 6.5 Every Workflow Starts with a Single Step
      • 6.6 Well, That Depends
      • 6.7 Rebuilding Certain Targets
      • 6.8 Discussion
      • 6.9 Further Reading
    • Chapter 7 Exploring Data
      • 7.1 Overview
      • 7.2 Inspecting Data and its Properties
      • 7.3 Computing Descriptive Statistics
      • 7.4 Creating Visualizations
      • 7.5 Further Reading
    • Chapter 8 Parallel Pipelines
      • 8.1 Overview
      • 8.2 Serial Processing
      • 8.3 Parallel Processing
      • 8.4 Distributed Processing
      • 8.5 Discussion
      • 8.6 Further Reading
    • Chapter 9 Modeling Data
      • 9.1 Overview
      • 9.2 More Wine Please!
      • 9.3 Dimensionality Reduction with Tapkee
      • 9.4 Clustering with Weka
      • 9.5 Regression with SciKit-Learn Laboratory
      • 9.6 Classification with BigML
      • 9.7 Further Reading
    • Chapter 10 Conclusion
      • 10.1 Let’s Recap
      • 10.2 Three Pieces of Advice
      • 10.3 Where To Go From Here?
      • 10.4 Getting in Touch
    • References
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