Practical data design tips from a data visualization expert of the modern age Data doesn’t decrease; it is ever–increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn’t it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full–color book, data visualization guru and author Nathan Yau uses step–by–step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step–by–step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data–native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.
Visualize This
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From the Author
From the Author: Telling Stories with Data
A common mistake in data design is to approach a project with a visual layout before looking at your data. This leads to graphics that lack context and provide little value. Visualize This teaches you a data-first approach. Explore what your data has to say first, and you can design graphics that mean something.
Visualization and data design all come easier with practice, and you can advance your skills with every new dataset and project. To begin though, you need a proper foundation and know what tools are available to you (but not let them bog you down). I wrote Visualize This with that in mind.
You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing, and most importantly be able to apply what you learn to your own data.
Three Data Visualization Steps:
1) Ask a Question
When you get a dataset, it sometimes is a challenge figuring out where to start, especially when it's a large dataset. Approach your data with a simple curiosity or a question that you want answered, and go from there.
2) Explore Your Data
A simple curiosity often leads to more questions, which are a good guide for what stories to dig into. What variables are related to each other? Can you see changes over time? Are there any features in the data that stand out? Find out all you can about your data, because the more you know what's behind the numbers, the better story you can tell.
3) Visualize Your Data
Once you know the important parts of your data, you can design graphics the best way you see fit. Use shapes, colors, and sizes that make sense and help tell your story clearly to readers. While the base of your charts and graphs will share many of the same properties – bars, slices, dots, and lines – the final design elements will and should vary by your unique dataset.
A common mistake in data design is to approach a project with a visual layout before looking at your data. This leads to graphics that lack context and provide little value. Visualize This teaches you a data-first approach. Explore what your data has to say first, and you can design graphics that mean something.
Visualization and data design all come easier with practice, and you can advance your skills with every new dataset and project. To begin though, you need a proper foundation and know what tools are available to you (but not let them bog you down). I wrote Visualize This with that in mind.
You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing, and most importantly be able to apply what you learn to your own data.
Three Data Visualization Steps:
1) Ask a Question
 (Click Graphic to See Larger Version) |
When you get a dataset, it sometimes is a challenge figuring out where to start, especially when it's a large dataset. Approach your data with a simple curiosity or a question that you want answered, and go from there.
2) Explore Your Data
 (Click Graphic to See Larger Version) |
A simple curiosity often leads to more questions, which are a good guide for what stories to dig into. What variables are related to each other? Can you see changes over time? Are there any features in the data that stand out? Find out all you can about your data, because the more you know what's behind the numbers, the better story you can tell.
3) Visualize Your Data
 (Click Graphic to See Larger Version) |
Once you know the important parts of your data, you can design graphics the best way you see fit. Use shapes, colors, and sizes that make sense and help tell your story clearly to readers. While the base of your charts and graphs will share many of the same properties – bars, slices, dots, and lines – the final design elements will and should vary by your unique dataset.
From the Back Cover
See your data in new ways
Our world is awash in data. To mean anything, it must be presented in a way that enables us to interpret, analyze, and apply the information. One of the best ways to do that is visually.
Nathan Yau is a pioneer of this innovative approach. In this book, he offers you dozens of ideas for telling your story with data presented in creative, visual ways. Open the book, open your mind, and discover an almost endless variety of ways to give your data new dimensions.
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Learn to present data with visual representations that allow your audience to see the unexpected
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Find the stories your data can tell
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Explore different data sources and determine effective formats for presentation
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Experiment with and compare different visualization tools
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Look for trends and patterns in your data and select appropriate ways to chart them
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Establish clear goals to guide your visualizations
Visit the companion web site at www.wiley.com/go/visualizethis for code samples, data files you can download, and interactive examples to show you how visualization works
From the Back Cover
See your data in new ways Our world is awash in data. To mean anything, it must be presented in a way that enables us to interpret, analyze, and apply the information. One of the best ways to do that is visually. Nathan Yau is a pioneer of this innovative approach. In this book, he offers you dozens of ideas for telling your story with data presented in creative, visual ways. Open the book, open your mind, and discover an almost endless variety of ways to give your data new dimensions. Learn to present data with visual representations that allow your audience to see the unexpected Find the stories your data can tell Explore different data sources and determine effective formats for presentation Experiment with and compare different visualization tools Look for trends and patterns in your data and select appropriate ways to chart them Establish clear goals to guide your visualizations Visit the companion web site at www.wiley.com/go/visualizethis for code samples, data files you can download, and interactive examples to show you how visualization works
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Customer Reviews
Most Helpful Customer Reviews
14 of 14 people found the following review helpful
By Finn Newick
Format:Paperback|Amazon Verified Purchase
This book concentrates on the visual presentation of numerical data. Most books on this subject that are already out there either focus on the data or the presentation but rather uniquely this one picks up on how these two ends of the spectrum meet in the middle. The techniques for extracting data from various sources, exploring the data and then selecting clear visualisations that enable further exploration and discovery are all presented in clear, practical examples that can be worked through. You won't find any meaningless data-porn here, just modern techniques for developing elegant and beautiful visualisations!
The book seems to be aimed at beginning or lower-intermediate data designers who are comfortable with either design or statistics (but not necessarily both) but it's good refreshing read even if you're already familiar with the content as the text contains many insightful thoughts from the author. I'm giving it 4 rather than 5 stars as it would've been nice if the book was rounded off with with one or two more challenging examples.
The book seems to be aimed at beginning or lower-intermediate data designers who are comfortable with either design or statistics (but not necessarily both) but it's good refreshing read even if you're already familiar with the content as the text contains many insightful thoughts from the author. I'm giving it 4 rather than 5 stars as it would've been nice if the book was rounded off with with one or two more challenging examples.
7 of 7 people found the following review helpful
Format:Paperback|Amazon Verified Purchase
I bought this book on a Friday with delivery the following day. All of that weekend I wasn't able to put the book down and even now I am always flicking through the pages for inspiration. It is a must read for anyone who handles data and/or prepares reports based on data, and is simply beautiful in its presentation. It is clear that every aspect of this book has been carefully considered, from the typeface to the page layout.
This book will open your eyes to what is possible once you move away from Microsoft Excel. As a professional analyst and data modeller, I have been using Excel for years but was growing frustrated with its limitations. In this book, Nathan Yau uses R, Python and Adobe Illustrator (though I personally prefer the open-source Inkscape equivalent) to show just what can be achieved with a little imagination and creativity.
I have given this five stars. Although it would have been nice to have more complex walk-throughs from raw data to final graphic as suggested in other reviews, to do so would have required the reader to have a solid foundation in R and Python programming. To include the required learning material in these programming languages so as to bring the reader up to speed as a programmer, as well as containing the excellent material it already does contain, would have required a book three or four times the thickness. If we were then to add a needed introductory statistics course into the book as well...
I think therefore to penalise the book for focusing purely on the creation of great looking graphics is a bit harsh especially when it says "Visualise", "design" and "visualisation" in the title.
That said there is a plethora of free PDF guides to R and Python (and Inkscape) legally available for download from the internet and of a high, publishable quality. These guides will take the reader from basic programming to intermediate level and beyond. See the documentation page on the R website or google "A Byte of Python" for an excellent, and free, beginners guide to Python programming.
So all in all this book will not teach you how to be a great R/Python programmer or statistician for that matter, but it will give you more than enough inspiration to motivate you away from Excel charts and towards teaching yourself powerful professional techniques that will make your presentations/reports stand out and make you a great data visualiser.
A simply beautiful book.
This book will open your eyes to what is possible once you move away from Microsoft Excel. As a professional analyst and data modeller, I have been using Excel for years but was growing frustrated with its limitations. In this book, Nathan Yau uses R, Python and Adobe Illustrator (though I personally prefer the open-source Inkscape equivalent) to show just what can be achieved with a little imagination and creativity.
I have given this five stars. Although it would have been nice to have more complex walk-throughs from raw data to final graphic as suggested in other reviews, to do so would have required the reader to have a solid foundation in R and Python programming. To include the required learning material in these programming languages so as to bring the reader up to speed as a programmer, as well as containing the excellent material it already does contain, would have required a book three or four times the thickness. If we were then to add a needed introductory statistics course into the book as well...
I think therefore to penalise the book for focusing purely on the creation of great looking graphics is a bit harsh especially when it says "Visualise", "design" and "visualisation" in the title.
That said there is a plethora of free PDF guides to R and Python (and Inkscape) legally available for download from the internet and of a high, publishable quality. These guides will take the reader from basic programming to intermediate level and beyond. See the documentation page on the R website or google "A Byte of Python" for an excellent, and free, beginners guide to Python programming.
So all in all this book will not teach you how to be a great R/Python programmer or statistician for that matter, but it will give you more than enough inspiration to motivate you away from Excel charts and towards teaching yourself powerful professional techniques that will make your presentations/reports stand out and make you a great data visualiser.
A simply beautiful book.
By J. Stockwell
Format:Kindle Edition|Amazon Verified Purchase
Filled full of great content for design tips, appropriate charts to use in certain situations. I read it quickly and was able to learn a lot of useful things.
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Some irresponsible advice
I was concerned that the intro to web-scraping makes no mention of the role of robots.txt and general netiquette around scraping. Read more
Published 3 months ago by Keef fan
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Basics to implementing Statistical Visualization
Although, it was not what I was looking for, I found this book interesting if you want to know the basics about implementing statistical information visualizations
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