Data Visualization with Processing

Zeeshan Lakhani
Sat & Sun, May 8 & 9, 2010, noon to 6pm
cost: $300/$390 

Data bombards us. We see it; we hope to organize it, find patterns and relationships in it, communicate it outward. Through the process of visualizing data, any form of data can tell a story and affect change. Processing has vitalized the world of data visualization by allowing artists to code (and learn how to code) and transfer their skills to the digital domain. Plus, itʼs open-source (free) and integrates seamlessly with a multitude of user-driven extensions, libraries, and classes.

This two-day session will be segmented into two parts. Day one will provide students with a fundamental understanding of the Processing language and environment, along with examples of its various capabilities. Day two will concentrate on approaches and techniques to obtain, cluster, and, most of all, represent data in a visually striking, interactive, and lucid way. The workshop will focus on using data from the web, which is remarkably stressless with Processing, and demonstrate how good visualizations can be relevant on a global scale.

Some of the topics to be covered include OpenGL, generative art, data retrieval from XML and feeds from The Echo Nest/Last.fm, tag clouds and other techniques for weighing data, diagram prototyping.

We will not provide meals or snacks for the course, but can point to lots of cheap dining places in the neighborhood. We also cannot provide accommodations, but can help with posting requests or bringing you in contact with other artists who might be able to help.

Zeeshan Lakhani is a web developer and applications programmer at the Liberty Science Center. He holds a B.A. in Moving Image Arts from the College of Santa Fe and an M.M. in Music Technology from New York University. His research deals with web and phone-related navigation systems, interactive data visualization, and music/ semantic/image information retrieval and processing. Currently, he is developing a concept for a visual engine to explore the learning path of scientifically-related user- searched topics.