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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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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. 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.

VC blog Posted: November 26th, 2014 | Author: Manuel Lima | Filed under: Uncategorized | No Comments » As some attentive users of Visual Complexity might have noticed, the number of projects featured on the website has slowly come to a halt, with the perpetual grand total of 777 being a grieving reminder of inactivity for well over a year. Today, If you go the the main page and look at the top right corner, you will see an invigorating new message: “Indexing 782 projects”. Of course I didn’t want to write this blog post to announce that five new projects have been added to the database. This recent addition is part of a larger plan I’ve been wanting to share with you for some time. In October 2015, Visual Complexity will celebrate its 10th Anniversary, a significant feat considering the life-span of many online projects, and an eerie memo that a long time has gone by since I launched the website after graduating from a MFA program at Parsons School of Design.

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. 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.

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?

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? 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:

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 How to choose the good chart The next time you face this question, the following chart can help you find your way. It shows the logical steps to find an appropriate chart. Whether you want to show a composition, a distribution, some relationship or if you want to compare some item, you will pick up a specific chart. It will be further determined by the time range, the number of items, … All in all, an awesome summary.

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