NRC BigData Education Workshop April 11-12, 2014, Washington DC

1. Data Science and Analytics Curriculum development at Rensselaer (and the Tetherless World Constellation) NRC BigData Education Workshop April 11-12, 2014, Washington DC Peter Fox (RPI and WHOI/AOP&E) pfox@cs.rpi.edu, @taswegian Tetherless World Constellation, http://tw.rpi.edu #twcrpi Earth and Environmental Science, Computer Science, Cognitive Science, and IT and Web Science

2. Data is a 1st class citizen http://thomsonreuters.com/content/press_room/science/686112

3. tw.rpi.edu • Future Web • Web Science • Policy • Social Hendler Research Themes • Xinformatics • Data Science • Semantic eScience • Data Frameworks Fox McGuinness • Semantic Foundations • Knowledge Provenance • Ontology Engineering Environments • Inference, Trust Multiple depts/schools/programs ~ 35 (Post-doc, Staff, Grad, Ugrad)

4. Govt. Data • Open • Linked • Apps Hendler/ Erickson Application Themes • Env. Informatics • Ecosystems • Sea Ice • Ocean imagery • Carbon Fox McGuinness • Platforms: • Bio-nano tech center • Exp. Media and Perf. Arts Ctr. • Center for Comput. Innovation • Institute for Data Exploration and • Applications http://idea.rpi.edu • Health Care/ Life Sciences • Population Science • Translational Med • Health Records

5. GIS4Science Data Analytics http://tw.rpi.edu/web/Courses Context Experience Data Information Knowledge Creation Gathering Presentation Organization Integration Conversation Data Science Xinformatics Semantic eScience 5 Web Science

6. I teach and am involved: • Data Science*, Xinformatics*, GIS for the Sciences*, Semantic eScience*, Data Analytics*, Sematic Technologies** • School of Science • ITWS and E&ES curriculum committees, SoS CC • E&ES international student advisor • Institute Faculty Fellow • Institute-wide • New Digital Humanities program • Institute for Data Exploration and Applications

7. Data Science/ Xinformatics Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work. Data science is helping scientists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce. At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in e-science collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set. At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data. In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems. This course will introduce informatics, each of its components and ground the material that students will learn in discipline areas by coursework and project assignments.

8. Modern informatics enables a new scale-free framework approach

9. Mediation; generations Borgmann et al., Cyber Learning Report, NSF 2008

10. Data Analytics Challenge

11. IT and Web Science • First IT academic program in U.S. • First web science degree program in U.S. • BS in ITWS (20 concentrations) and MS in IT (10 concentrations) • PhD in Multi-Disciplinary Sciences • http://itws.rpi.edu

13. CHANGES TO THE MASTER’S IN INFORMATION TECHNOLOGY PROGRAM • In Spring 2013 the MS in IT core curriculum was revised to include Data Analytics. • Networking core classes were replaced with Data Analytics core classes: Data Science, Database Mining, X-informatics, and Data Analytics (a new class offered in Spring 2014). • The MS in IT program also added two new concentrations: Data Science and Analytics and Information Dominance. • The Information Dominance concentration was developed for a new Navy program that will be educating a select group of 5-10 naval officers a year with the skills needed for military cyberspace operations. Two officers started in Fall 2013 and three began in Spring 2014.

14. MS in IT Required Core Courses * For the research track, replace ITWS-6300 Business Issues for Engineers and Scientists with one of the two semester courses ITWS-6980 Master’s Project or ITWS-6990 Master’s Thesis. Advanced Core options for students who have previously completed a Core Course

15. Two New MS in IT Concentrations

16. Also at RPI • Data Science Research Center and Data Science Education Center (dsrc.rpi.edu, 2009) • http://www.rpi.edu/about/inside/issue/v4n17/datacenter.html • Over 45: research faculty, post-docs, grad students, staff, undergraduates… • Data is one of the Rensselaer Plan’s five thrusts • Other key faculty • Fran Berman (Center for Digital Society and RDA) • Bulent Yener (DSRC Director) • Jin Hendler (IDEA Director)

17. data.rpi.edu (v0.1, 2009)

18. Soon…

19. More RPI Curriculua • Environmental Science with Geoinformatics concentration • Bio, geo, chem, astro, materials - informatics • GIS for Science • Master of Science – Data Science?? (pending) • Multi-disciplinary science program - PhD in Data and Web Science • DATUM: Data in Undergraduate Math! (Bennett) • Missing – intermediate statistics • Graphs – significant potential here – must teach!

20. 5-6 years in… • 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 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 ***

21. Challenging the “Heroic” Science Paradigm This national and international has drawn attention to the need for a reassessment of priorities to recognize that, in the new data era, the burden of making data and information usable shifts from the user to the provider.

22. And thus … in <10 years