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7 Essential Books on Data Visualization & Computational Art

7 Essential Books on Data Visualization & Computational Art
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. Processing, the open-source programming language and integrated development environment invented by Casey Reas and Ben Fry in 2001, is easily the most fundamental framework underpinning the majority of today’s advanced data visualization projects. Recommended by: Casey Reas Since 2005, (a longtime Brain Pickings favorite) have been algorithmically scrobbling the social web to capture occurrences of the phrases “I feel” and “I am feeling” harvesting human sentiment around them by recording the full context in which the phrase occurs. Reviewed in full here. Recommended by: Jer Thorp Recommended by: Moritz Stefaner Recommended by: Wes Grubbs Related:  DataVis Tutorials - Principles

Which Visualization Type Should I Use? Ever spend several hours putting together what you think is a killer presentation, only to step back and realize that your visuals aren’t quite cutting it? That was me this weekend. There I was working on, what I thought was a killer project when my friend came in and said how my charts just really sucked. After researching tips to create better visuals, I found this one article that ended up being super helpful – Best Practices: Maximum Elements For Different Visualization Types by Drew Skau. Pie Charts According to Skau, “Pie Charts are among the most popular visualizations, but they aren’t appropriate for more than seven categories.” Bar and Column Charts Did you know that you can have too many bars in a bar chart? Line Charts Much like pie charts, having too many data sets graphed out in a line chart can make it difficult to read. Colors You guessed it. Image Source: www.iStockphoto.com & Visual.ly Related

Data Flow 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. style books. The book is divided into six sections and Onlab describe them thus: Datasphere Using the circle as the first, perfect shape, impossible to achieve by human hand, it derives the tension between what is achieved and what could be achieved. For me the Datanets are beautiful, but perhaps the most obvious ways of displaying data. My favourites are the Datascapes and Datology sections because of the more human aspect to them (and I think conjure up those childhood memories of being fascinated by books like this). Rating: Buy Data Flow from Amazon.com , Amazon.co.uk

Newsletter. ASA Statistics Computing and Graphics Home The Computing and Graphics Newsletter is distributed to members of the Statistical Computing and Statistical Graphics Sections of the ASA. Their annual dues assist section activities. If you are an ASA member, but do not belong to either of these Sections, please consider joining. Editorial Staff The Newsletter is produced by two volunteer editors, one from each of the Computing and Graphics Sections, with articles from both ASA members and non-members. Online Issues As a service to our members, we make PDF versions of the complete Newsletter available for downloading. Volume 23, Number 1. Featured articles: Computing News from SAS by Rick Wicklin News from R Studio by Tareef Kawaf Amazon EC2, Big Data and High-Performance Computing by Jay Emerson and Xiaofei Wang Volume 22, Number 1. Visualization: It's More than Pictures! Volume 21, Number 2. barNest: Illustrating nested summary measures by Jim Lemon and Ofir Levy You say "graph invariant," I say "test statistic" by Carey E.

Data Science Kit - Deals  Data Science Starter Kit The Tools You Need to Get Started with Data From basic statistics to complex modeling and large-scale analytics, the Data Science Starter Kit outlines a clear path to mastering data and gets you started with essential tools, key algorithms and methods, and a survey of the hottest languages and frameworks in today's ecosystem. Buy any two titles and get the 3rd Free with discount code: OPC10 Or, get them all for $209.20 (60% savings) Data Science for Business: Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. Ebook: $33.99 Doing Data Science: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. Ebook: $38.99 Video: $119.99 Video: $159.99

Concept-driven versus Data-driven visualizations In several previous posts, I explored how Clark & Weibe's (2000) idea of data-driven versus concept-driven visualizations plays out in geosciences and how this distinction could be important as we help students learn to learn from visualizations. This semester, in my course on "Teaching & Learning Concepts in Earth Sciences," students found and documented visualizations that afford insights via spatial thinking about a topic they are working on for a semester long project. Applying the idea of data-driven versus concept-driven visualizations to this image collection surfaced several additional nuances to the categorization schema. There were classic examples of concept-driven visualizations, in which an artist or scientist-artist has depicted an idea, a conceptual model: One of my previous posts pointed out that concept driven visualizations are at high risk of overspecificity, because in many cases the artist must choose which of several (or many) possibilities to put down on the page.

Designing Data Visualizations: Julie Steele, Noah Iliinsky Book Description Publication Date: 2 Oct 2011 Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Frequently Bought Together Customers Who Bought This Item Also Bought Product Description Book Description Intentional Communication from Data to Display About the Author Noah Illinsky has spent the last several years thinking about effective approaches to creating diagrams and other types of information visualization. What Other Items Do Customers Buy After Viewing This Item? 2.7 out of 5 stars Most Helpful Customer Reviews

A Case Study In How Infographics Can Bend The Truth We’ve made the point time and time again that charts and graphs, though they feel official and true, can lie. Rarely do you get to see that at work, but the good folks at Hyperakt have sent us a prime case study in infographic deception. The subject, of course, is politics--and in particular, the raging debate over whether the rich should be made to pay more taxes. "Using the same data, very different stories can be told depending on different agendas," says Deroy Peraza, one of the founders of Hyperakt. A story from the Wall Street Journal's far-right op-ed page gets us started, with a chart showing how much taxable income is made by Americans ranging from the rich to poor: Looking at that, the conclusion seems glaringly obvious: The rich don’t make so much money! The chart most certainly does not demonstrate the Journal’s point. And look closer: The left side of the chart deals with people who make between $0 and $50K. Whoa whoa whoa! Top image: S.

Books 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, 24 MB) Errata (Updated 8 January 2014) This book is an introduction to the ideas of computer programming within the context of the visual arts. The majority of the book is divided into tutorial units discussing specific elements of software and how they relate to the arts. Essays by Alexander R. Interviews with Jared Tarbell, Martin Wattenberg, James Paterson, Erik van Blockland, Ed Burton, Josh On, Jeurg Lehni, Auriea Harvey and Michael Samyn, Mathew Cullen and Grady Hall, Bob Sabiston, Jennifer Steinkamp, Ruth Jarman and Joseph Gerhardt, Sue Costabile, Chris Csikszentmihalyi, Golan Levin and Zachary Lieberman, and Mark Hansen.

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