As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent…
Grafana is the leading open source project for visualizing metrics. Supporting rich integration for every popular database like Graphite, Prometheus and InfluxDB.
Mithilfe von SAP Lumira können Sie Ihre Daten mit anderen Augen betrachten, indem Sie auf einer Drag-and-Drop-Oberfläche Visualisierungen erstellen. Kombinieren und analysieren Sie Daten aus Excel und anderen Unternehmensquellen, um schnell und einfach neue Erkenntnisse zu gewinnen – ohne Skripte, vordefinierte Abfragen oder Berichte
The QlikView Business Discovery platform delivers true self-service business intelligence that empowers business users and drives innovative decision making
Datavisualization.ch Selected Tools is a collection of tools that we, the people behind Datavisualization.ch, work with on a daily basis and recommend warmly.
Datavisualization.ch Selected Tools is a collection of tools that we, the people behind Datavisualization.ch, work with on a daily basis and recommend warmly.
DataDepot is a set of tools for collaboratively uploading, sharing, and analyzing data. You can use DataDepot to track personal data, to explore public data, and to engage with scientific data.
here's a lot of great information out there about politics — votes, lobbying records, campaign finance reports. Unfortunately, it's split across a dozen different web sites and often hidden behind confusing interfaces. We're pulling all of that together and letting you explore it in one elegant, unified interface. (Plus, we're sharing all the results so you can come up with new ways to explore it.)
DbVisualizer offers features for database developers, analysts and DBAs. Read more about Database Object Management, SQL Script Management, Query Builder and more.
VisualEyes is web-based authoring tool developed at the University of Virginia to weave images, maps, charts, video and data into highly interactive and compelling dynamic visualizations. VisualEyes enables scholars to present selected primary source materials and research findings while encouraging active inquiry and hands-on learning among general and targeted audiences. It communicates through the use of dynamic displays – or "visualizations" – that organize and present meaningful information in both traditional and multimedia formats, such as audio-video, animation, charts, maps, data, and interactive timelines. The effective use of the visualizations can reveal and illuminate relationships between multiple kinds of information across time and space far more effectively than words alone.
Datavisualization.ch Selected Tools is a collection of tools that we, the people behind Datavisualization.ch, work with on a daily basis and recommend warmly.
blprnt.com is a home online for digital artist and designer Jer Thorp. It is an exhibiition of generative projects & experiments with evolution as well as a space for discussion and learning. Jer Thorp is an artist and educator working out of Vancouver, Canada. His work has been exhibited internationally and he is a regular speaker at conferences and events around the world.
Appunta is a Framework for the Android platform that allows us not only to easily show geopositional information to the user, but also to create new ways of showing this information or modifying the existing ones.
Basically, you have a set of POI (Points Of Interest) located in a map (thus, with a latitude, longitude, and optionally, an altitude), and you need to show these POI and their related information to the user.
Appunta allows you, out of the box, to represent this information in two different ways, a radar or an augmented reality view. But, you can modify these components to show data in other ways or create new ways of visualizing this information.
Appunta is Open Source and anybody can freely use it. So, what are you waiting for?
D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. As a trivial example, you can use D3 to generate a basic HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.
Processing is an electronic sketchbook for developing ideas. It is a context for learning fundamentals of computer programming within the context of the electronic arts.
We’ve been thrilled with the all support we’ve been getting from users who are helping us rate the tempo of music tracks in our Speedo experiment, thanks! Now we’d like to ask you to help us with another fun music experiment for a new project called Audio Flowers. We are currently doing some research into new techniques to measure structural change (or “complexity”) in rhythm, harmony and timbre directly from mp3 files. The measurements we take from a song are then summarised to produce a little image: an Audio Flower like the one below.
That’s why at Last.fm we’ve programmed our computers to listen to music. We fed them around 15,000 tracks from the UK singles charts between 1960 and 2008 and discovered some fascinating results we’d like to share with you. It all starts with the discovery that just before the middle of the 70s something in the data changed…
Here’s a visualization concept I came up with a while back to look at the way search engines and word-of-mouth affects hit frequency on the iBiblio web-traffic log. iBiblio consists of around 420 sites. Each one of the circles you see represents one of the websites. The size of each pie slice inside grows with respect to the number of hits by individual search engines (see the legend for which ones). The size of the circle grows with respect to the overall number of hits by people other than search engines. Hits are counted by number of unique incoming IP addresses per day. Links get drawn between cliques of websites where more than 1/4th of the unique IP addresses are the same on that day, meaning, more or less, that those sites often share traffic. The total amount of data was around 10TB and the visualization took about a day to process into a static animation. The original is meant to run on a wall-sized (16′x9′) or on our specialized visualization dome.
The first chapter introduces the problem space in terms of making sense of very large, complex datasets and outlines the vision for visual analytics. T
N. Padhy, D. Mishra, and R. Panigrahi. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2 (3):
43-58(June 2012)
A. Oka, H. Masuhara, T. Imai, and T. Aotani. Companion to the First International Conference on the Art, Science and Engineering of Programming, page 26:1--26:7. New York, NY, USA, ACM, (2017)
I. Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 280--284. New York, NY, USA, ACM, (2000)