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#MakeOverMonday : improving how we visualize and analyze data, one chart at a time /
Chapter 4 Keep It Simple; What Is Simplicity?; Simplicity in Design; Simplicity in Layout and Positioning; Simplicity in Colors and Icons; Simplicity in Analysis; Getting Started with New Data; Start Simple; Know When to Stop; Simplicity in Storytelling; Finding Insights; Focusing on a Key Message;...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Intro; #MakeoverMonday; Contents; Foreword; Acknowledgments; From Andy and Eva; From Andy; From Eva; About the Authors; Andy Kriebel; Eva Murray; Part I; Introduction; What Is Makeover Monday?; How Did Makeover Monday Start?; The Community Project; The Andys: Makeover Monday 2016; The Murray/Cotgreave Swap: Makeover Monday 2017; The Next Phase: Makeover Monday 2018; Pillars of Makeover Monday; Developing Technical Skills; Building a Data Visualization Portfolio; Learning and Inspiration; Networking; Demonstrating Leadership; Making an Impact; How to Use this book; Part II
- Chapter 1 Habits of a Good Data AnalystApproaching Unfamiliar Data; Identify the Challenges; Gain Insights from Metadata; Explore the Data; Analysis versus Visualization; Take Your Time; Build Context Through Additional Research; Read the Available Information; Seek Additional Information; Find Insights; Educating Your Audience; Communicate Clearly; Ask Questions; Summary; Chapter 2 Data Quality and Accuracy; Working with Incomplete Data; Incomplete Data; Missing Data; Excluding Data; Tips for Working with Incomplete or Missing Data; Overcounting Data; Sense-Checking Data; Trump's Tweets
- Is Puerto Rico a State?Is the Data Aggregable?; Adult Obesity in the United States; Averages of Averages; Substantiating Claims with Data; Summary; Chapter 3 Know and Understand the Data; Using Appropriate Aggregations; Can the Data Be Aggregated?; Basic Aggregation Types; Explaining Metrics; Know Your Audience; Using Appropriate Metrics; Creating New Metrics to Tell a Different Story; Identifying and Correcting Mistakes; Time Series Analysis; Univariate Time Series; Visualizing Seasonality; Using Moving Averages for Smoothing; Variance from a Point in Time; Cycle Plots; Calendar Heat Map
- Planning the LayoutDesigning for Mobile; Know Your Audience; Information Displays; Color Choices; Use of White Space; Keep It Simple; Bringing It All Together; Using Visual Cues for Additional Information; Using Icons and Shapes; Proper Attributions; Go Easy on the Shapes; Storytelling; Finding a Story and Sticking to It; Long-Form Storytelling; Think Like a Data Journalist; Reviewing Your Work to Improve Its Quality; Take a Step Back; Ask a Friend; Viz Review; Summary; Chapter 7 Trying New Things; Developing a Sharing Culture; Circular Charts; Images from Dot Plots; Patterns and Shapes