Books for DataVis
The Making Data Meaningful guides are intended as a practical tool to help managers, statisticians and media relations officers in statistical organizations use text, tables, charts, maps and other devices to bring statistics to life for non-statisticians. Part 1: A guide to writing stories about numbers The first guide provides guidelines and examples on the use of effective writing techniques to make data meaningful. Making Data Meaningful.
I understand that data visualization is a hot topic at the moment. So it is not surprising that publishers cannot wait to capitalize on this attention. However, while many recent books on visualization seem to have been hastily written, I found this one especially disappointing. For example: Contrary to their own advice ("Are you using color to represent quantity? Stop it
by Maria Popova What 12 million human emotions have to do with civilian air traffic and the order of the universe. I’ve spent the past week being consistently blown away at the EyeO Festival of data visualization and computational arts, organized by my friend Jer Thorp , New York Times data artist in residence, and Dave Schroeder of Flashbelt fame.
If there is one resource we’re not short of these days it is data. We’re swimming in the stuff and generating it all the time. Making visual sense of all that data requires a fine balance between complexity and simplicity. Data Flow: Visualising Information in Graphic Design is an absolutely beautiful collection of some of the finest examples of the art. Before I get onto the content I have to talk about the production.
"The success of companies like Google, Facebook, Amazon, and Netflix, not to mention Wall Street firms and industries from manufacturing and retail to healthcare, is increasingly driven by better tools for extracting meaning from very large quantities of data. 'Data Scientist' is now the hottest job title in Silicon Valley.
Processing: A Programming Handbook for Visual Designers and Artists Casey Reas and Ben Fry (Foreword by John Maeda). Published August 2007, MIT Press. 736 pages. Hardcover. » Order from Amazon.com Downloads: Table of Contents and Index (PDF, 500 KB) Sample Chapters with Contents and Index (PDF, 7.6 MB) All code examples in the book (ZIP, 15 MB) Errata (Updated 22 April 2010)