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Data Science at the Command Line
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Chapter 10 Conclusion
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2019-08-10 10:02:52
Chapter 10 Conclusion
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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 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
References
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 2 Getting Started
2.1 Overview
2.2 Installing the Docker Image
2.3 Essential GNU/Linux Concepts
2.4 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 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 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 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
Preface
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
Welcome
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