Peter Fox of Rensselaer Polytechnic Institute on their move to make every student data literate.

1. Data Science for Every
Student at RPI
Peter Fox (RPI), @taswegian, pfox@cs.rpi.edu, http://tw.rpi.edu
Evolving Education … 2016, Las Vegas NV
Sun. Oct. 23 2016

2. Agenda
• Cyber-learning and mediation
• Data-Information-Knowledge Curriculum ~ 8 years in… degree program
• Data Science education – anatomy and physiology
• Sediment …. Wait for it …
• Rensselaer Core Curriculum and Data

3. From: C. Borgman, 2008, NSF Cyberlearning Report
5 generations of mediation
6th Generation
All these generations of
mediation are in effect as we
learn, conduct research, and
collaborate
Smart Text Agents
Smart Data Agents
Relationship and Association Rules
Cognitive Collaboration

4. 4
Data Information Knowledge
Context
Presentation
Organization
Integration
Conversation
Creation
Gathering
Experience
Data Science Xinformatics Semantic eScience
Web Science
GIS4Science
Data Analytics
http://tw.rpi.edu/web/Courses

5. Institute-wide Program Curriculum
Engineering
Computer
Networking
Computer Hardware
… and more
Science
Data Science
Information Security
Web Technologies
Medicine
Science Informatics
. . . and more
Management
MIS
Entrepreneurship
Finance
HASS
Arts
Cognitive Computing
Economics
Pre-Law
Psychology
. . . and more
CONCENTRATION (8 courses)
ITWS CORE
Special Interest
Introduction to Information
Technology and Web Science
Web Systems Development
Web Science Systems Development
Computer Science 1/Data Structures
Technical Track
Social/Cultural/Political Impacts
Human Computer Interaction
Managing IT Resources
Project-based Capstone OR Thesis
(Professional OR Research track)
http://itws.rpi.edu

6. June 7, 2016 RPI Advanced Professional Studies 6
Degree and Concentrations
• Master of Science in Information Technology
• > 10 Concentrations:
• Data Science and Analytics
• Web Science
• Information Dominance
• Total of 10 courses, 30 credit hours
• Five core courses
• Three concentration courses
• One elective
• One Capstone or Project course
Degree and Concentrations
• Bachelor of Science in Information
Technology and Web Science
• > 20 Concentrations:
• Data Science and Analytics
• Web Technologies
• Cognitive Computing
• Total of 124-128 credit hours
• Science and humanities core courses
• Eight concentration courses
• 24 credits of electives
• Culminating Experience Capstone course

7. June 7, 2016 RPI Advanced Professional Studies 7
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
• Application of computer science courses such as data mining,
machine learning, and change detection by uncovering unexpected
patterns in data.

8. Anatomy
Physiology
Overused Venn diagram of the intersection of skills
needed for Data Science (Drew Conway)
Missing Anatomy

9. Data Science (4000-level, 6000-level, since 2009)
 Anatomy (as an individual)
 Data Life Cycle – Acquisition, Curation
and Preservation
 Data Management and Products
 ALL Forms of Analysis, Error and
Uncertainty Assessment
 Technical tools and standards

10. Data Science (4000-level, 6000-level)
 Physiology (in a group)
 Definition of Science Hypotheses, Guiding
Questions
 Finding and Integrating Datasets
 Presenting Analyses and Viz.
 Presenting Conclusions

11. • Data Dexterity: Institute Wide Initiative (Lead: Prof. K. Bennett, Assoc. Dir. IDEA)
• Data Awareness core curriculum for all undergraduates
• Require data-intensive courses for all students
• Add concentrations, certificates, minors to many of our majors
• Building interdisciplinary courses and programs
• eg. courses launched in: data ethics, cognitive computing, Big Data projects
• eg. digital ethnography project, data analytics masters, Increased campus participation
in Production/Installation/Presentation (PIP) program
• Data Interdisciplinary Challenge Intelligent Technology Exploration (Data-INCITE)
Laboratory
• Based on Multidisciplinary Design Lab self-sustaining model
• Work directly with established and emerging companies
• Work with MITRE and w/Govt Partners (AFRL, ARL, etc.)
• Create data-related coop/internship opportunities
• Benefit to corporate partners and to our students
Transformative Educational Impact
11
Develop Data Dexterity in Every Rensselaer Student

12. Overarching outcomes (proposed in 2015) will be achieved via:
Common/Core
Courses (HASS
and Science)
Disciplinary
Courses
Co-/ Extra-
Curricular
Activities
Rensselaer Core Curriculum

13. Rensselaer Core Curriculum
SCIENCE Core (24cr)
• Math (8cr)
• Data Intensive (DI) Course (approved Science [or HASS] courses)
Similar to the Communication Intensive (CI) sequence, the Data Intensive
Sequence would consist of two parts – Part 1 would be a prequalified Data
Intensive introductory level science or HASS course that includes a DI unit
dedicated to developing an awareness and exposure to repositories and uses
of data sets and an introduction to basic tools associated with data analytics.
Most if not all of these courses would be synchronous with program specified
foundational science courses. Part 2 Data Intensive courses should be part of
the Disciplinary Requirements.

14. Rensselaer Core Curriculum
DISCIPLINARY (PROGRAM) CORE
• Communication Intensive Course (approved disciplinary course)
• Data Intensive Course Sequence (approved disciplinary course)
• Collaborative Experience
• Hands-on Experience
• Interdisciplinary Experience
• Culminating Experience
A two-part Data Intensive sequence is proposed. These are not unique courses
about Data Analytics but include units on data analytics alongside other content.
Similar to CI sequence – requiring a DI committee that approves courses both for
part 1 and for part 2.

15. Data INCITE Lab in BETA Classroom – Summer 2016
Projects:
• Tokyo Electron Limited
• Global Foundries
• RPI Microbiome Project
• RPI Circadian Rhythms
20 Sophomore and Junior Math
Majors

16. Predictive Analytics Deepens Story

17. Data INCITE Lab in AE217 Classroom – Fall 2017
Projects:
• General Electric
• HBI Solutions, Inc
• Global Foundries
• RPI Jefferson Project

18. Rensselaer Core Curriculum
CO- / EXTRA-CURRICULAR CORE
• Summer Reading for entering students
• Externship: Away Experience
• International Experience or,
• Community Engagement, or
• Externally based research experience, or
• Industry Engagement (internship or co-op)
• Leadership / Civic Engagement Experience
• Rensselaer Enrichment Program (REP)
• Academic Events (8) and
• Cultural Events (8)

19. So who are we talking about?
19
http://images2.fanpop.com/image/photos/9400000/Lt-Commander-Data-star-trek-the-next-generation-9406565-1694-2560.jpg

20. Call to Action – Data Science
 Data Science across the curriculum
 Same as “Calculus”
 And … Intro to Statistics
 Data Management is second nature
 Like operating an instrument
 Openness/ sharing is the natural state
 As-a-whole, the Data Scientist works
collaboratively and is recognized and
rewarded by peers and organizations

21. Call to Action – Data Analytics
 Institutions to provide reliable, high-functionality data
infrastructures that facilitate analytics
 Provision of intermediate to advanced Statistics to
undergraduates and early graduate students
 Well-curated datasets are made widely available
along with developed models and validation statistics
 All results are under continuous scrutiny, are
traceable and verifiable

22. Same for Cognitive Computing 6000->4000->2000
Needed evolution of cognitive
systems where humans, many
humans are in the loop – bringing
generations 1, 2 and 3 together
with generations 3, 4, 5 and now
6.

23. Data Science for Every
Student at RPI
Peter Fox (RPI), @taswegian, pfox@cs.rpi.edu, http://tw.rpi.edu
Evolving Education… 2016, Las Vegas NV
Sun. Oct. 23 2016