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VizWiz - Data Visualization Done Right

VizWiz - Data Visualization Done Right
UPDATE – 10-Apr-3014: I received some feedback from both Jonathan Drummey and Joe Mako about this blog post and some of its inaccuracies. There are a couple of key notes: My intent was to show how you can compare the 7-day averages of two time periods. In this example, I’m calling this a Year over Year calculation, but really it’s a comparison versus 365 days ago. Small, but important distinction.

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Visual Business Intelligence For data sensemakers and others who are concerned with the integrity of data sensemaking and its outcomes, the most important book published in 2016 was Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil. This book is much more than a clever title. It is a clarion call of imminent necessity. Data can be used in harmful ways. This fact has become magnified to an extreme in the so-called realm of Big Data, fueled by an indiscriminate trust in information technologies, a reliance on fallacious correlations, and an effort to gain efficiencies no matter the cost in human suffering.

The Dataviz Design Process: 7 Steps for Beginners Does data visualization leave you feeling like this? If so, this beginner-level post is for you! Data visualization requires two skillsets: technical skills to create visualizations in a software program and critical thinking skills to match your visualization to your audience’s information needs, numeracy level, and comfort with data visualization.

Calendar View Visualisation In official statistics we’re used to dealing with highly aggregated data. To visualise those, bar-, line- and pie charts are standard tools. But there is a whole other side to visualisation where it is used to recognize patterns, outliers or errors in individual data. VC blog Posted: February 19th, 2014 | Author: Manuel Lima | Filed under: Uncategorized | No Comments » As many readers might have noticed, from my first and most recent book, I’m slightly obsessed with medieval information design, and the remarkable work of many our visualization forefathers, such as Isidore of Seville (ca. 560–636), Lambert of Saint-Omer (ca. 1061–ca. 1125), or Joachim of Fiore (ca. 1135–1202). An important figure in this context was the German historian and cartographer Hartmann Schedel (1440–1514). In 1493, in the city of Nuremberg, Germany, Schedel published a remarkable, densely illustrated and technically advanced incunabulum (a book printed before 1501), entitled the Nuremberg Chronicle. Also know as Liber Chronicarum (Book of Chronicles), this universal history of the world was compiled from older and contemporary sources, and comprised 1,809 woodcuts produced from 645 blocks. You can read more about Taschen’s copy here and here.

Gallery: U.S. Federal Budget Back to Gallery Home Let’s begin with some tilted 3D pie charts and work our way toward a more revealing visualization. Here are the above 1993 and 2012 pie chart pairs, with Receipts and Outlays converted to flows in two separate Sankey diagrams: More Ways to Visualize Data: Charts Maps are awesome. Adding charts to a map is even more awesome. In addition to mapping data at Geocommons, users can now visualize the same data by utilizing our newly introduced charts. The backbone of these charts was created using g.Raphael, which is based on Raphael‘s JavaScript graphics library.

Principal Component Analysis step by step In this article I want to explain how a Principal Component Analysis (PCA) works by implementing it in Python step by step. At the end we will compare the results to the more convenient Python PCA()classes that are available through the popular matplotlib and scipy libraries and discuss how they differ. The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information. Here, our desired outcome of the principal component analysis is to project a feature space (our dataset consisting of n x d-dimensional samples) onto a smaller subspace that represents our data "well". A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data "best". What is a "good" subspace?

Graphics in R Please direct questions and comments about these pages, and the R-project in general, to Dr. Tom Philippi. Introduction One of the strengths of R is graphical presentation of data. One consequence is that most pdf introductions to R and introductory books on R include chapters on the basics of R graphics, or include graphical examination of the data integrated with the statistical analyses. Data science We’ve all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean?

Tools on Datavisualization A Carefully Selected List of Recommended Tools 07 May 2012 Tools Flash, JavaScript, Processing, R When I meet with people and talk about our work, I get asked a lot what technology we use to create interactive and dynamic data visualizations. To help you get started, we have put together a selection of the tools we use the most and that we enjoy working with. Read more

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