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Beautiful Visualization: Looking at Data through the Eyes of Experts Paperback – Jul 1 2010
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Looking at Data Through the Eyes of Experts
About the Author
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.
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.
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Top Customer Reviews
This is actually a collection of small books, none of which would be worth printing on its own. The pictures aren't beautiful, and few of them are as intuitive or as easily interpreted as the authors think they are. I didn't find anything in here that I'd use.
Most Helpful Customer Reviews on Amazon.com (beta)
I found all of the illustrated chapters and differing expert viewpoints on visual storytelling with data extremely valuable. A slight nit I would have with this phenomenal work is that I would have placed Jessica Hagy's chapter entitled Visualization: Indexed derived from her blog on visual storytelling first because of its explanatory power. But, perhaps the editors felt that a different emphasis was appropriate in that this is an O'Reilly collection emphasizing Visual Storytelling with The Computer as the Preeminent Tool.
A Beautiful Coffee Table Book that will serve both scientists and graphic artists.
HCI and IT Consultant formerly on the Senior Staff of Arthur D. Little Inc. and Digital Equipment Corporation
If you have any interest in this field, I can't recommend this book highly enough. Fantastic, useful information.
The book does an excellent job of visiting various applicable domains. There is enough variety to keep you occupied. Its covers visualizing social datasets(link-node), hierarchical datasets(trees), categorical datasets(relational), time-varying datasets and other most commonly found datasets in scientific and information visualizations. Most chapters are well written with plenty of visuals. If you are an ardent fan of visuals you probabaly have seen everything in this book at one place or another. I think a significant part of the information is derived from papers or other well known publications. You can find almost anything by taking the visualcomplexity website as a starting point. However, the book can save you the research and is an excellent introductory text to expand your knowledge on visualizations.
That being said, I feel its little expensive for its size and as most content is there on the web already, O'Reilly should have listed it at slightly lesser cost. In case you are not going to buy, its a 5-star book no doubt.
The chapters are written by different people, each with different methodologies and tools that they use to visualize data. Several chapters include sample code or suggested applications you can use to create visualizations, most of them open source. A key thread throughout the course of this book is using visualization to tell stories that may not otherwise be told or realized by simple data evaluation.
I work with creating visualizations myself, and for the book was most useful in its demonstration of the creative process. Each chapter features a data artist and one particular piece of their work, chronicled from inspiration to final product. It offers a rare glimpse into the working mind of an artist, going through their various revisions and explaining what worked in each round, and the reasons for their creative decisions.
I recommend this book for anyone interested in visualizations, from the casual observer to professional data artist. I've found it immensely helpful as a source of inspiration.
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