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
Table of contents
- 1. Getting to an Effective Visualization
2. From Questions to Tasks
- Example: Identifying Good Movie Directors
- Making a Question Concrete
- Breaking Down a Task
- Returning to the Example: Exploring Different Definitions
- How Specific Does the Process Get?
- Making Use of Results
- Conclusion: A Well-Operationalized Task
- Further Reading
- 3. Data Counseling, Exploration, and Prototyping
- 4. Components of a Visualization
5. Single Views
- Overall Perceptual Concerns
- Question: How Is a Measure Distributed?
- 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?
- Question: How Are Objects Related to Each Other in a Network or Hierarchy?
- Question: Where Are Objects Located?
- Question: What Is in This Text?
- Further Reading
- 6. Multiple and Coordinated Views
- 7. Case Study 1: Visualizing Telemetry to Improve Software
- 8. Case Study 2: Visualizing Biological Data
- 9. Conclusions
- Title: Making Data Visual
- Release date: January 2018
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491928462
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