- Eurozone debt - http://www.bbc.co.uk/news/business-15748696
- Wikileaks US embassy cables - http://datavisualization.ch/datasets/wikileaks-us-embassy-cables/
- Stopping SOPA and PIPA - http://visual.ly/stop-sopa
- Auto accident statistics in Britain - http://www.bbc.co.uk/news/magazine-16631597
- A snapshot of the rapidly changing world of computing, communications and technology - http://www.nytimes.com/interactive/2011/12/06/science/1206-world.html?ref=science
- Words by the millions - http://www.nytimes.com/2012/03/25/business/words-by-the-millions-sorted-by-software.html?_r=1&ref=technology
- county health ratings - http://www.countyhealthrankings.org/app
- live wind map - http://hint.fm/wind/index.html
- Factual - http://www.nytimes.com/2012/03/25/business/factuals-gil-elbaz-wants-to-gather-the-data-universe.html?ref=technology
- worldwide health data - http://www.youtube.com/watch?v=jbkSRLYSojo&feature=player_embedded
- Obama's budget proposal - http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html?emc=eta1
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)
- Part 1 - http://www.npr.org/2011/11/29/142521910/the-digital-breadcrumbs-that-lead-to-big-data?ps=rs
- Part 2 - http://www.npr.org/2011/11/30/142893065/the-search-for-analysts-to-make-sense-of-big-data
New York Times article from December, 2011 on bioinformatics and visualization, MicJ
- At some point nyt.com supported a "viz lab" where people could use their data sets to build their own visualizations. I can't find a current reference to this now. 20 January 2012
- IBM's Many Eyes -
- David McCandless: The beauty of data visualizations (TED) - http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html
- What we learned from 5 million books (TED) - http://www.ted.com/talks/what_we_learned_from_5_million_books.html
- Google's ngram interface: http://books.google.com/ngrams/
- Big data
- Dakota Lambert
- Tristan Wright
- Elena Sergienko
- Diana Ainembabazi
- Mikel Qafa
- Ivan Babic
- Leif DeJong
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?
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
- Which parts of statistics do people need to know?
- correlation for PCA
- What linear algebra do people need to know?
- matrix operations for PCA
- R under Linux/OSX
7) Possible sources for data sets
- John Iverson
- Mike Deibel
- Kathy Milar