Nathan Yau 加州大學洛杉磯分校統計學專業在讀博士、超級數據迷，專注於數據可視化與個人數據收集。他曾在《紐約時報》、CNN、Mozilla和SyFy工作過，認為數據和信息圖不僅適用於分析，用來講述與數據有關的故事也非常閤適。Yau的目標是讓非專業人士讀懂並用好數據。他創建瞭一個設計、可視化和統計方麵的博http://flowingdata.com，你可以從中欣賞到他最新的數據可視化實驗作品。

嚮怡寜 交互和視覺設計師、搖滾樂手，同時還熱衷於翻譯和寫作。著有《Flash組件、遊戲、SWF加解密》及《就這麼簡單：Web開發中的可用性和用戶體驗》，譯有《奇思妙想：15位計算機天纔及其重大發現》、《瞬間之美：Web界麵設計如何讓用戶心動》、《網站設計解構：有效的交互設計框架和模式》、《網站搜索設計：兼顧SEO及可用性的網站設計心得》等書。他認為“一個不會彈吉他的設計師不是個好譯者”。

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.

From the Author: Telling Stories with Data

Author Nathan Yau 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

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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

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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

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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.