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, @taswegianpfox@cs.rpi.edu Tetherless World Constellation, http://tw.rpi.edu #twcrpihttp://tw.rpi.edu Earth and Environmental Science, Computer Science, Cognitive Science, and IT and Web Science

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

3. tw.rpi.edu Research 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)

4. 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 Platforms: Bio-nano tech center Exp. Media and Perf. Arts Ctr. Center for Comput. Innovation Institute for Data Exploration and Applications http://idea.rpi.eduhttp://idea.rpi.edu

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

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 10

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

12. Technical Track Courses Concentrations Computer Engineering Track 1)ECSE-2610 Computer Components and Operations 2)ENGR-2350 Embedded Control 3)ECSE-2660 Computer Architecture, Networking and Operating Systems Civil Engineering Computer Hardware Computer Networking (hardware focus) Mechanical/Aeronautical Eng. Computer Science Track 1)CSCI-2200 Foundations of Computer Science 2)CSCI-2300 Introduction to Algorithms 3)CSCI-2500 Computer Organization Cognitive Science Computer Networking (software focus) Information Security Machine and Computational Learning Information Systems Track 1)CSCI-2200 Foundation of Computer Science 2)CSCI-2500 Computer Organization 3)Four credits from the following:  CSCI-2220 Programming in Java (2 credits)  CSCI-2961 Program in Python (2 credits)  CSCI-2300 Introduction to Algorithms (4 credits)  ITWS-49XX Web Systems Development II (4 credits) Arts Communication Economics Entrepreneurship Finance Management Information Systems Medicine Pre-law Psychology STS Web Science Track 1)CSCI-2200 Foundations of Computer Science 2)CSCI-2500 Computer Organization 3)One of the following:  CSCI-49XX Web Systems Development II  Web/Data Course approved by ITWS Curriculum Committee Data Science Science Informatics Web Technologies

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. IT Core AreaCourse NumberCourse Title Term(s) Offered Database SystemsCSCI-4380Database SystemsFall/Spring Data AnalyticsITWS-6350Data ScienceFall Software Design and Engineering CSCI-4440Software Design and DocumentationFall ITWS-6400X-InformaticsSpring Management of Technology* ITWS-6300 Business Issues for Engineers and Scientists (Professional Track Only) Fall/Spring Human Computer Interaction COMM-6420Foundations of HCI UsabilityFall COMM-696XHuman Media InteractionSpring 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 IT Core AreaCourse NumberCourse Title Term(s) Offered Database Systems CSCI-6390Database MiningFall ITWS-6350Data ScienceFall ITWS-696XSemantic E-ScienceFall Data Analytics CSCI-6390Database MiningFall ITWS-6400X-InformaticsSpring ITWX-696XData AnalyticsSpring Software Design CSCI-6500Distributed Computing Over the InternetFall ECSE-6780Software Engineering IIFall ITWS-696XSemantic E-ScienceFall Management of Technology MGMT-6080Networks, Innovation and Value CreationFall MGMT-6140Information Systems for ManagementSpring Human Computer Interaction COMM-6620Information ArchitectureSpring COMM-6770User-Centered DesignFall COMM-696XInteractive Media DesignSummer

15. ConcentrationCourse NumberCourse NameTerm(s) Offered Data Science and Analytics Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with an entire methodology. Key topics include: advanced statistical computing theory, multivariate analysis, and application of computer science courses such as data mining and machine learning and change detection by uncovering unexpected patterns in data. Select two or three of the following courses: ITWS-6350 Data ScienceFall ITWS-6400 X-InformaticsSpring ITWS-696X Data Analytics Spring ITWS-696X Semantic E-Science Fall ITWX-696X Advanced Semantic Technologies* Spring If only two of the above were chosen, select one more of the following courses: COMM-6620 Information Architecture Spring CSCI-4020Computer AlgorithmsSpring CSCI-4150Introduction to AIFall CSCI-6390Database MiningFall CSCI-4220 or CSCI- 6220 Network Programming or Parallel Algorithm Design Spring ISYE-4220 Optimization Algorithms and Applications Fall ISYE-6180 Knowledge Discovery with Data Mining Spring MGMT-696X Technology Foundations for Business Analytics Fall MGMT-696X Predictive Analytics Using Social Media Spring ConcentrationCourse NumberCourse NameTerm(s) Offered Information Dominance The Information Dominance concentration prepares students for careers designing, building, and managing secure information systems and networks. The concentration includes advanced study in encryption and network security, formal models and policies for access control in databases and application systems, secure coding techniques, and other related information assurance topics. The combination of coursework provides comprehensive coverage of issues and solutions for utilizing high assurance systems for tactical decision-making. It prepares students for careers ranging from secure information systems analyst, to information security engineer, to field information manager and chief information officer. It is also appropriate for all IT professionals who want to enhance their knowledge of how to use pervasive information in situational awareness, operations scenarios, and decision-making. Select two or three of the following courses: ISYE-6180 Knowledge Discovery with Data Mining Spring CSCI-6960 Cryptography and Network Security I Fall ITWS-4370Information System SecuritySpring CSCI-4650Networking Laboratory I Fall/Spri ng MGMT-7760Risk ManagementFall ISYE-4310 Ethics of Modeling for Industrial Systems Engineering Fall If only two of the above were chosen, select one more of the following courses: CSCI-6390Database MiningFall CSCI-6968 Cryptography and Network Security II Spring CSCI-4660Networking Laboratory II Fall/Spri ng ECSE-6860 Evaluation Methods for Decision Making Fall ISYE-6500 Information and Decision Technologies for Industrial and Service Systems Fall/Spri ng CSCI-496X Computational Analysis of Social Processes Fall 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/datacente r.htmlhttp://www.rpi.edu/about/inside/issue/v4n17/datacente r.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