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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

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: & 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 ,

Visual Complexity: Mapping Patterns for the Information Age by Maria Popova What the basis of Buddhism has to do with Jack Kerouac, poverty in Italy and Alice in Wonderland. Data visualization is a running theme of visual literacy here, and Manuel Lima has been one of its biggest advocates since 2005 when, shortly after graduating from the Parson School of Design, he launched VisualComplexity — an ambitious portal for the visualization of complex networks across a multitude of disciplines, from biology to history to the social web. This month marks the highly anticipated release of Visual Complexity: Mapping Patterns of Information — a rigorously researched, beautifully designed, thoughtfully curated anthology of the world’s most compelling work at the intersection of these two relatively nascent yet increasingly powerful techno-cultural phenomena, network science and information visualization. Philipp Steinweber and Andreas Koller Similar Diversity, 2007 Marco Quaggiotto Knowledge Cartography, 2008 The tree of the Two Advents Brain and Body

Ernst Haeckel Un article de Wikipédia, l'encyclopédie libre. Ernst Hæckel Ernst Haeckel en 1860. Ernst Heinrich Philipp August Hæckel (Potsdam, le - Iéna, le ), était un biologiste, philosophe et libre penseur allemand. Il a fait connaître les théories de Charles Darwin en Allemagne et a développé une théorie des origines de l'homme. Ernst Haeckel contribua beaucoup par ses écrits à la diffusion de la théorie de l'évolution. Biographie[modifier | modifier le code] En 1857 et 1858, Ernst Haeckel obtint son doctorat de médecine, puis il obtint son autorisation d'exercer la médecine (Approbation). En 1861, après seulement une année de pratique, il obtint son habilitation et un poste de conférencier (Privat-docent) en anatomie comparée à l’université d'Iéna avant de devenir, l’année suivante professeur extraordinaire d’anatomie comparée à l’institut de zoologie de l’université. En 1862, il épousa sa cousine Agnès Sethe. En 1881-1882, il parcourut les mers tropicales et Ceylan.

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

Gephi, an open source graph visualization and manipulation software Color as Data: Visualizing Color Composition by Maria Popova Abstracting glossy magazines, or what pie charts have to do with the Mona Lisa. We love data visualization and color. Computational artist Mario Klingemann, a.k.a. The pie charts represent the distribution of dominant colors within a circle area. Designer Shahee Ilyas‘ amusingly minimalist deconstruction of country flags by color composition is an absolute treat. Besides the playful irreverence, the project reveals some curious patterns of color choice, raising even more curious questions about color symbolism. Data viz superheroes Martin Wattenberg and Fernanda Viegas have taken their visualization magic to the world of fashion photography. To create the images in luscious, we began with a series of magazine advertisements for luxury brands. Brain Pickings has a free weekly newsletter and people say it’s cool. Share on Tumblr