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.
- 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
Who Is This Book For?
Who Are We?
Overview of Chapters
How to Contact Us
1. Getting to an Effective Visualization
Getting to Insight
Hotmap: Making Decisions with Data
Where Visualization Is Useful
2. From Questions to Tasks
Example: Identifying Good Movie Directors
Making a Question Concrete
A Concrete Movie Question
Breaking Down a Task
When Tasks Lead to New Questions
Returning to the Example: Exploring Different Definitions
How Specific Does the Process Get?
Making Use of Results
Conclusion: A Well-Operationalized Task
3. Data Counseling, Exploration, and Prototyping
Technique 1: Data Counseling
Conducting Contextual Interviews
Technique 2: Exploring the Data
Technique 3: Rapid Prototyping for Design
The Range of Prototypes
4. Components of a Visualization
Dimensions and Measures
Example: International Towing & Ice Cream
Types of Data
Transforming Between Dimension Types
Dimensionality Reduction and Clustering
5. Single Views
Overall Perceptual Concerns
Question: How Is a Measure Distributed?
Density Plot for Two Dimensions
Question: How Do Groups Differ from Each Other?
Question: Do Individual Items Fall into Groups? Is There a Relationship Between Attributes of Items?
Question: How Does an Attribute Vary Continuously?
Line and Area Charts
Question: How Are Objects Related to Each Other in a Network or Hierarchy?
Treemap and Sunburst
Question: Where Are Objects Located?
Question: What Is in This Text?
6. Multiple and Coordinated Views
Multiform Views and Dashboards
Axis Alignment and Scale Consistency
Interacting with Multiple Linked Views
MLVs and the Operationalization Process
7. Case Study 1: Visualizing Telemetry to Improve Software
Determining How to Compare Builds
Comparing Distributions to Understand “Better”
Turning Back to the Data
Final UI for High-Level Goals
8. Case Study 2: Visualizing Biological Data
Setting the Context
Zooming in a Level
Improving the Existing Approach
Similarity, Not Outliers
A Final Version
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