curriculum development at the tetherless world constellation - peter fox - rdap12
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Curriculum development at the Tetherless World Constellation – the
days after the “Day One” initiative
RDAP
March 22-23, 2012, New Orleans, LA
Peter Fox (RPI and WHOI) pfox@cs.rpi.eduTetherless World Constellation
tw.rpi.edu
Themes
Future Web•Web
Science•Policy•Social
Xinformatics•Data Science
•Semantic eScience
•Data Frameworks
Semantic Foundations•Knowledge Provenance
•Ontology Engineering Environments•Inference, Trust
Hendler
Fox
McGuinness
Multiple depts/schools/programs ~ 35 (Post-doc, Staff, Grad, Ugrad)
Application Themes
Govt. Data•Open
•Linked•Apps
Env. Informatics•Ecosystems
•Sea Ice•Ocean imagery
•Carbon
Health Care/ Life Sciences•Population Science•Translational Med
•Health Records
Hendler/ Erickson
Fox
McGuinness/Luciano
Platforms:Bio-nano tech centerExp. Media and Perf. Arts Ctr.Comp. Ctr. Nano. Innov.
Data Intensive
Also at RPI
• Data Science Research Center and Data Science Education Center
• http://www.rpi.edu/about/inside/issue/v4n17/datacenter.html– Over 35 research faculty, 5 post-docs, ? grad
students
• Data is one of Rensselaer Plans’ five thrusts
• Other key faculty– Fran Berman (VPR)– Jim Myers (Director CCNI)
http://tw.rpi.edu/web/Courses
5
Data Information Knowledge
Context
PresentationOrganization
IntegrationConversation
CreationGathering
Experience
Data Science Xinformatics Semantic eScienceWeb Science
Curriculum
• Web Science and IT – undergrad, and MSc. and PhD. (with science concentrations)
• Environmental Science with Geoinformatics concentration
• Bio, geo, chem, astro, materials - informatics
• GIS for Science
• Master of Science – Data Science (pending)
• Multi-disciplinary science program (2012) PhD in Data and Web Science
Dayz after…
• Science and interdisciplinary from the start!– Not a question of: do we train scientists to be
technical/data people, or do we train technical people to learn the science
– It’s a skill/ course level approach that is needed
• We must teach methodology and principles over technology *
• Data science must be a skill, and natural like using instruments, writing/using codes
• Team/ collaboration aspects are key **• Foundations and theory must be taught ***
Modern informatics enables a new scale-free framework approach
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