Difference between revisions of "Visualizations"

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== The Project ==
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Math/CS 484 -- The goal of our Ford/Knight project is to distill and organize the principles of visualizing large data sets. Modern science is often done by small groups of people that come from diverse backgrounds, e.g. a mathematician, a biologist, and a computer scientist. We plan to solicit input in the form of example data sets to work with from each of the natural and social science departments on campus. This work will provide a foundation for a course, or course module, which we hope to offer in the future. Must see instructor for registration.
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== Examples ==
 
== Examples ==
 
* Eurozone debt - http://www.bbc.co.uk/news/business-15748696
 
* Eurozone debt - http://www.bbc.co.uk/news/business-15748696

Revision as of 10:42, 11 April 2012

The Project

Math/CS 484 -- The goal of our Ford/Knight project is to distill and organize the principles of visualizing large data sets. Modern science is often done by small groups of people that come from diverse backgrounds, e.g. a mathematician, a biologist, and a computer scientist. We plan to solicit input in the form of example data sets to work with from each of the natural and social science departments on campus. This work will provide a foundation for a course, or course module, which we hope to offer in the future. Must see instructor for registration.

Examples

Press

NPR did a couple of interesting segments on Big Data, visualizations, and the search of mathematicians and others who can do that stuff. (December, 2011)

New York Times article from December, 2011 on bioinformatics and visualization, MicJ

Other

Presentations

Keywords

  • infographics
  • Big data
  • work flow(s)

The People

  1. Dakota Lambert
  2. Tristan Wright
  3. Elena Sergienko
  4. Diana Ainembabazi
  5. Mikel Qafa
  6. Ivan Babic
  7. Leif DeJong

Tools

The Plan

1) Planning items

  • Are there any field trip opportunities?
  • Figure-out what books to order
  • Figure-out what are the likely conference opportunities?
  • Are there any other tools besides R that we should be considering?
    • GRASS?

2) Things to learn

  • Is there a somewhat canonical process or technique that one can reliably apply to go from readings -> data -> information -> visualization?
  • How to utilize geocoding attributes?
  • How to utilize timestamp attributes?

3) Things to read

4) Things to do during the class

5) Questions

  • Which parts of statistics do people need to know?
    • correlation for PCA
  • What linear algebra do people need to know?
    • matrix operations for PCA

6) Tools

  • R under Linux/OSX

7) Possible sources for data sets

  • John Iverson
    • turtle birthing data
    • phylogenetic reconstruction
  • Mike Deibel
  • Kathy Milar
  • Meg Streepy
    • GPlates - visualizing plate tectonics