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
Chapter 1. Getting To An Effective Visualization
Chapter 2. From Questions To Tasks
Chapter 3. Data Counseling, Exploration, And Prototyping
Chapter 4. Components Of A Visualization
Chapter 5. Single Views
Chapter 6. Multiple And Coordinated Views
Chapter 7. Case Study 1: Visualizing Telemetry To Improve Software
Chapter 8. Case Study 2: Visualizing Biological Data
Chapter 9. Conclusions