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Making Data Visual

Book Description

You have a mound of data front of you and a suite of computation tools at your disposal. Which parts of the data actually matter? Where is the insight hiding? If you’re a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way.

When incorporated into the process early and often, iterative visualization can help you refine the questions you ask of your data. Authors Danyel Fisher and Miriah Meyer provide detailed case studies that demonstrate how this process can evolve in the real world.

You’ll learn:

  • The data counseling process for moving from general to more precise questions about your data, and arriving at a working visualization
  • The role that visual representations play in data discovery
  • Common visualization types by the tasks they fulfill and the data they use
  • Visualization techniques that use multiple views and interaction to support analysis of large, complex data sets

Table of Contents

  1. Preface
    1. Who Is This Book For?
    2. Who Are We?
    3. Overview of Chapters
    4. Acknowledgments
    5. O’Reilly Safari
    6. How to Contact Us
  2. 1. Getting to an Effective Visualization
    1. Getting to Insight
    2. Hotmap: Making Decisions with Data
    3. Where Visualization Is Useful
    4. Further Reading
  3. 2. From Questions to Tasks
    1. Example: Identifying Good Movie Directors
    2. Making a Question Concrete
      1. A Concrete Movie Question
    3. Breaking Down a Task
      1. When Tasks Lead to New Questions
    4. Returning to the Example: Exploring Different Definitions
    5. How Specific Does the Process Get?
    6. Making Use of Results
    7. Conclusion: A Well-Operationalized Task
    8. Further Reading
  4. 3. Data Counseling, Exploration, and Prototyping
    1. Technique 1: Data Counseling
      1. Identifying Stakeholders
      2. Conducting Interviews
      3. Conducting Contextual Interviews
    2. Technique 2: Exploring the Data
    3. Technique 3: Rapid Prototyping for Design
      1. The Range of Prototypes
      2. Eliciting Feedback
    4. And, Repeat
    5. Conclusion
  5. 4. Components of a Visualization
    1. Dimensions and Measures
      1. Example: International Towing & Ice Cream
      2. Dimensions
      3. Types of Data
      4. Transforming Between Dimension Types
      5. Dimensionality Reduction and Clustering
    2. Examining Actions
  6. 5. Single Views
    1. Overall Perceptual Concerns
    2. Question: How Is a Measure Distributed?
      1. Histogram (Categorical)
      2. Histogram (Quantitative)
      3. Smoothed Histogram
      4. Box Plot
      5. Density Plot for Two Dimensions
    3. Question: How Do Groups Differ from Each Other?
      1. Bar Chart
      2. Pie Chart
      3. Heatmap
    4. Question: Do Individual Items Fall into Groups? Is There a Relationship Between Attributes of Items?
      1. Scatterplot
    5. Question: How Does an Attribute Vary Continuously?
      1. Line and Area Charts
    6. Question: How Are Objects Related to Each Other in a Network or Hierarchy?
      1. Node-Link View
      2. Adjacency Matrix
      3. Tree View
      4. Treemap and Sunburst
    7. Question: Where Are Objects Located?
      1. Geographical Map
    8. Question: What Is in This Text?
      1. Word Cloud
    9. Conclusion
    10. Further Reading
      1. Relevant Articles
      2. Datasets
  7. 6. Multiple and Coordinated Views
    1. Small Multiples
    2. Scatterplot Matrices
    3. Overview+Detail
    4. Multiform Views and Dashboards
    5. Overlays
    6. Axis Alignment and Scale Consistency
    7. Interacting with Multiple Linked Views
    8. MLVs and the Operationalization Process
    9. Conclusion
    10. Further Reading
      1. Datasets
  8. 7. Case Study 1: Visualizing Telemetry to Improve Software
    1. Introduction
    2. Project Background
    3. The Data
    4. Determining How to Compare Builds
    5. Comparing Distributions to Understand “Better”
    6. Multiple Scenarios
    7. Sketching Dashboards
      1. Turning Back to the Data
    8. Final UI for High-Level Goals
      1. Additional Visualizations
    9. Conclusion
      1. Acknowledgments
    10. Further Reading
  9. 8. Case Study 2: Visualizing Biological Data
    1. Background
    2. Setting the Context
      1. Zooming in a Level
    3. Improving the Existing Approach
    4. Similarity, Not Outliers
    5. A Final Version
    6. Conclusion
    7. Further Reading
  10. 9. Conclusions
  11. Index