Its primary emphasis is the science of visualization. The first, “From data to visualization,” describes different types of plots and charts, such as bar graphs, scatter plots, or pie charts. The annotated bibliography at the end of the book includes pointers to appropriate texts covering these topics. The book also does not provide any instruction on how to make figures with existing visualization softwares or programming libraries. Therefore, throughout this book, I will use the words “visualization” and “figure” somewhat interchangeably. The book does not cover interactive visuals or movies, except in one brief section in the chapter on visualizing uncertainty. I am specifically covering the case of static visualizations presented in print, online, or as slides. Because data visualization is a vast field, and in its broadest definition could include topics as varied as schematic technical drawings, 3D animations, and user interfaces, I necessarily had to limit my scope for this book. The book attempts to cover the key principles, methods, and concepts required to visualize data for publications, reports, or presentations. It is my goal to provide useful information to both groups. Designers, on the other hand, may prepare visualizations that look beautiful but play fast and loose with the data. However, they may not have a well developed sense of visual aesthetics, and they may inadvertantly make visual choices that detract from their desired message. In my experience, scientists frequently (though not always!) know how to visualize data without being grossly misleading. If a figure contains jarring colors, imbalanced visual elements, or other features that distract, then the viewer will find it harder to inspect the figure and interpret it correctly. Good visual presentations tend to enhance the message of the visualization. At the same time, a data visualization should be aesthetically pleasing. If one number is twice as large as another, but in the visualization they look to be about the same, then the visualization is wrong. A data visualization first and foremost has to accurately convey the data. The challenge is to get the art right without getting the science wrong and vice versa.
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