Difference between revisions of "BigDataChallenge2014"

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2) Anaconda - a full-featured distribution of Python  https://store.continuum.io/cshop/anaconda/
 
2) Anaconda - a full-featured distribution of Python  https://store.continuum.io/cshop/anaconda/
  
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===Other Resources===
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SciNet Analysts are happy to give students feedback regarding their analyses.  They can be contacted at [mailto:bigdatachallenge@scinet.utoronto.ca bigdatachallenge@scinet.utoronto.ca]
  
 
== Information Sessions ==
 
== Information Sessions ==
  
 
* [http://wiki.scinethpc.ca/wiki/images/c/c0/BigDataChallenge.pdf Slides] from the second information session.
 
* [http://wiki.scinethpc.ca/wiki/images/c/c0/BigDataChallenge.pdf Slides] from the second information session.

Revision as of 09:51, 6 November 2014

Links for the Big Data Challenge 2014


Computational tools and resources available to the students participating in the Challenge

SAS resources to be provided to students

1) SAS University Edition: http://www.sas.com/en_us/software/university-edition.html

2) SAS On-Demand For Academics – This is a free cloud version of 5 of our products including Enterprise Miner – our foundation Data Mining tool. I will start a course for the competition http://www.sas.com/govedu/edu/programs/od_academics.html

3) Free E-Learning from U of T – See this PDF and the Access code is G70072789. There are a twelve different courses for students learn how to use SAS.

4) http://www.lexjansen.com – Great website for students to search and learn about different techniques of Analytics.


Open Source resources

1) R - Revolution R Open, from Revolution Analytics http://mran.revolutionanalytics.com/download/

2) Anaconda - a full-featured distribution of Python https://store.continuum.io/cshop/anaconda/


Other Resources

SciNet Analysts are happy to give students feedback regarding their analyses. They can be contacted at bigdatachallenge@scinet.utoronto.ca

Information Sessions

  • Slides from the second information session.