Data visualization is an efficient and effective medium for communicating large amounts of information. This book aims to demystify the design process for those who are already comfortable with data analysis, showing the reader how to encode information visually via a linear process of decision-making.
Data visualization is an efficient and effective medium for communicating large amounts of information. This book aims to demystify the design process for those who are already comfortable with data analysis, showing the reader how to encode information visually via a linear process of decision-making.
Noah Illinsky has spent the last several years thinking about effective approaches to creating diagrams and other types of information visualization. He also works in interface and interaction design, all from a functional and user-centered perspective. Before becoming a designer he was a programmer for several years. He has a master's in Technical Communication from the University of Washington, and a bachelor's in Physics from Reed College. Julie Steele is an Editor at O'Reilly currently working on titles related to Python, SQL, PHP, web frameworks and CMS, databases (relational and non-relational), big data and cloud computing, and data visualization. She's also interested in data transparency and open government, and recently completed a master's degree in political science at Rutgers University.
Inhaltsangabe
1. Preface 2. What Will You Design? * Chapter 1: Classifications of Visualizations * Chapter 2: Source Trinity: Ingredients of Successful Visualizations 3. How Should You Design It? * Chapter 3: Determine Your Goals and Supporting Data * Chapter 4: Choose Appropriate Visual Encodings * Chapter 5: First, Place * Chapter 6: Apply Your Encodings Well * Additional Resources * Checklist
1. Preface 2. What Will You Design? * Chapter 1: Classifications of Visualizations * Chapter 2: Source Trinity: Ingredients of Successful Visualizations 3. How Should You Design It? * Chapter 3: Determine Your Goals and Supporting Data * Chapter 4: Choose Appropriate Visual Encodings * Chapter 5: First, Place * Chapter 6: Apply Your Encodings Well * Additional Resources * Checklist
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