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Data Points: Visualization That Means Something 1st Edition
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From the Back Cover
Reveal the story your data has to tell
To create effective data visualizations, you must be part statistician, part designer, and part storyteller. In his bestselling book Visualize This, Nathan Yau introduced you to the tools and programming techniques for visualization. Now, in Data Points, he explores the thinking process that helps you create original, meaningful visualizations that your audience will both understand and remember. Here's how to make your data mean something.
- Discover what data is and what you can learn from it
- Learn how to explore your data, find the story, and bring it to life
- Understand visualization as a medium that lets you present and express meaning in data
- Tap into your creative side and determine the most effective way to tell your story
- Compare tools for exploration and analysis
- Allow data, the story, and your goals to dictate visualization techniques with geometry, traditional charts, maps, color, art, and even humor
About the Author
Nathan Yau has a PhD in statistics and is a statistical consultant who helps clients make use of their data through visualization. He created the popular site FlowingData.com, and is the author of Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, also published by Wiley.
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He found helpful ideas in this book. The graphics are wonderful, quality of the book visually is excellent. The author has a PhD in statistics from UCLA and has a site at FlowingData. Some of the content in this book includes: *discover what data is and what you can learn from it *learn how to explore your data, find the story, and bring it to life *understand visualization that lets you present and express meaning in data *tap into your creative side and determine the most effective way to tell your story *compare tools for exploration and analysis *allow data, the story, and your goals to dictate visualization techniques with geometry, charts, maps, color, art and humor.
This book was helpful for his needs, and he is pleased with it.
A solidly useful work, hence three stars, but misses its full potential
I also have Yau's first book. This is a better book, both from writing technique and organization and content perspective. The first book had specific code examples, where this book focuses more on high-level concepts that can be applied to all graphics.
I would like to see a one-page tear-out "cheat sheet" summary of the recommendations in future editions.
Obviously recommended. -- also check out flowingdata.com if you want to see excerpts and style.
But who was this book written for? Not for data analysts, whose primary tools is graphs and tables. The vast majority of the examples in this book are strange visualizations that you can't create easily in Excel or PowerPoint: chloropleth maps, sankey diagrams, astronomical maps.
Not for data visualization students. The commentary and analysis of each piece is poor, more like rambling banter than any serious attempt to break things down into their core principles that can be applied to their practical work.
Not for artists of infographics, again because there are few core principles shared (in fact, Yau seems to be saying "there are no hard and fast rules!", perhaps to distance himself from dogmatic writers like Tufte and Few) and no graphic design tips and tricks that would help you emulate these beautiful graphics.
So who is the audience? It reads like a series of lengthy blog posts, with Yau yammering on about something only he cares about. I finished the book and my primary thought was "what did I just learn?" The unfortunate answer - nothing.
Data Points enumerates fundamental visual cues like position, area, shape and color (hue); detailing when each is useful and for what purpose. It also discusses coordinate systems (Cartesian, radial and geographic) and when each is appropriate.
As a novice just entering this field, this book gave me the fundamental vocabulary necessary to think about data visualization and information design.
Yau did a great job explaining in layman's terms the complexities of data visualizations. I began reading this book with really no experience in data visualizations. This book does a great job providing readers with the big picture then explains how to get there starting with basic concepts. I feel I learned a lot from reading this book and am more equipped to make clean and informing data visualizations.