Difference between revisions of "BigDataChallenge2014"
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== Links for the Big Data Challenge 2014 == | == Links for the Big Data Challenge 2014 == | ||
| − | * The [http://cysjournal.ca/page/bigdatachallenge Challenge Website] | + | |
| + | * The [http://iecarus.com/trainings-and-events/big-data-challenge/ Challenge Website] | ||
| + | <!-- * The [http://cysjournal.ca/page/bigdatachallenge Challenge Website] --> | ||
* The [http://support.scinet.utoronto.ca/BigDataChallenge2014/BIG_DATA_CHLNG.xlsx Data Set] | * The [http://support.scinet.utoronto.ca/BigDataChallenge2014/BIG_DATA_CHLNG.xlsx Data Set] | ||
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== Computational tools and resources available to the students participating in the Challenge == | == Computational tools and resources available to the students participating in the Challenge == | ||
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4) http://www.lexjansen.com – Great website for students to search and learn about different techniques of Analytics. | 4) http://www.lexjansen.com – Great website for students to search and learn about different techniques of Analytics. | ||
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===Open Source resources=== | ===Open Source resources=== | ||
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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=== | ||
| + | * 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] | ||
| + | * General inquiries about the challenge should be directed to [mailto:bigdata@cysjournal.ca bigdata@cysjournal.ca] | ||
| + | |||
| + | == Information Sessions == | ||
| + | |||
| + | * [http://wiki.scinethpc.ca/wiki/images/c/c0/BigDataChallenge.pdf Slides] from the second information session. | ||
Latest revision as of 17:41, 5 February 2015
Links for the Big Data Challenge 2014
- The Challenge Website
- The Data Set
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
- General inquiries about the challenge should be directed to bigdata@cysjournal.ca
Information Sessions
- Slides from the second information session.