Transcript Peter Fox - National Academy of Sciences

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) [email protected], @taswegian
Tetherless World Constellation, http://tw.rpi.edu #twcrpi
Earth and Environmental Science, Computer Science, Cognitive Science, and
IT and Web Science
Data is a 1st class citizen
http://thomsonreuters.com/content/press_room/science/686112
2
tw.rpi.edu
Research
Themes
Future Web
•Web Science
•Policy
Hendler
•Social
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)
Govt. Data
•Open
•Linked
•Apps
Application
Themes
Env. Informatics
•Ecosystems
•Sea Ice
•Ocean imagery
•Carbon
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.edu
Health Care/ Life Sciences
•Population Science
•Translational Med
•Health Records
GIS4Science
Data Analytics Context
http://tw.rpi.edu/web/Courses
Experience
Data
Creation
Gathering
Information
Presentation
Organization
Knowledge
Integration
Conversation
Data Science Xinformatics Semantic
eScience
5
Web Science
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
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 biomedicalinformatics 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.
Modern informatics enables a new
scale-free framework approach
Mediation; generations
Borgmann et al., Cyber Learning Report, NSF 2008
Data Analytics Challenge
10
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
Technical Track Courses
Concentrations
Computer Engineering
Track
1)
2)
3)
ECSE-2610 Computer Components and Operations
ENGR-2350 Embedded Control
ECSE-2660 Computer Architecture, Networking and
Operating Systems
Civil Engineering
Computer Hardware
Computer Networking (hardware focus)
Mechanical/Aeronautical Eng.
Computer Science Track
1)
2)
3)
CSCI-2200 Foundations of Computer Science
CSCI-2300 Introduction to Algorithms
CSCI-2500 Computer Organization
Cognitive Science
Computer Networking (software focus)
Information Security
Machine and Computational Learning
Information Systems Track
1)
2)
3)




CSCI-2200 Foundation of Computer Science
CSCI-2500 Computer Organization
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)
2)
3)


CSCI-2200 Foundations of Computer Science
CSCI-2500 Computer Organization
One of the following:
CSCI-49XX Web Systems Development II
Web/Data Course approved by ITWS Curriculum
Committee
Data Science
Science Informatics
Web Technologies
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.
MS in IT Required Core Courses
IT Core Area
Database Systems
Course Number
CSCI-4380
Course Title
Database Systems
Term(s)
Offered
Fall/Spring
Data Analytics
ITWS-6350
Data Science
Fall
Software Design and
Engineering
CSCI-4440
Software Design and Documentation
Fall
ITWS-6400
Spring
COMM-6420
X-Informatics
Business Issues for Engineers and Scientists
(Professional Track Only)
Foundations of HCI Usability
COMM-696X
Human Media Interaction
Spring
Management of
Technology*
Human Computer
Interaction
ITWS-6300
Fall/Spring
Fall
* For the research track, replace ITWS-6300 Business Issues for Engineers and Scientists with one of the two semester courses ITWS6980 Master’s Project or ITWS-6990 Master’s Thesis.
Advanced Core options for students who have previously completed a Core Course
IT Core Area
Database Systems
Data Analytics
Software Design
Management of
Technology
Human Computer
Interaction
Course Number
Course Title
Term(s)
Offered
CSCI-6390
Database Mining
Fall
ITWS-6350
Data Science
Fall
ITWS-696X
Semantic E-Science
Fall
CSCI-6390
Database Mining
Fall
ITWS-6400
X-Informatics
Spring
ITWX-696X
Data Analytics
Spring
CSCI-6500
Distributed Computing Over the Internet
Fall
ECSE-6780
Software Engineering II
Fall
ITWS-696X
Semantic E-Science
Fall
MGMT-6080
Networks, Innovation and Value Creation
Fall
MGMT-6140
Information Systems for Management
Spring
COMM-6620
Information Architecture
Spring
COMM-6770
User-Centered Design
Fall
COMM-696X
Interactive Media Design
Summer
Two New MS in IT Concentrations
Concentration
Concentration
Course Number
Course Name
Course Name
Term(s)
Offered
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.
Term(s)
Offered
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:
Data
Science and
Analytics
Course Number
ITWS-6350
Data Science
Fall
Select two or three of the following courses:
ITWS-6400
X-Informatics
Spring
ISYE-6180
Knowledge Discovery with Data
Mining
Spring
ITWS-696X
Data Analytics
Spring
CSCI-6960
Cryptography and Network
Security I
Fall
ITWS-696X
Semantic E-Science
Fall
ITWS-4370
Information System Security
Spring
ITWX-696X
Advanced Semantic
Technologies*
CSCI-4650
Networking Laboratory I
Fall/Spri
ng
MGMT-7760
Risk Management
Fall
Ethics of Modeling for Industrial
Systems Engineering
Fall
Spring
If only two of the above were chosen, select one more of
the following courses:
Information
Dominance
COMM-6620
Information Architecture
Spring
ISYE-4310
CSCI-4020
Computer Algorithms
Spring
CSCI-4150
Introduction to AI
Fall
If only two of the above were chosen, select one more of the
following courses:
CSCI-6390
Database Mining
Fall
CSCI-4220 or CSCI6220
Network Programming
or Parallel Algorithm
Design
Spring
ISYE-4220
Optimization Algorithms
and Applications
Fall
Knowledge Discovery
with Data Mining
Spring
MGMT-696X
Technology Foundations
for Business Analytics
Fall
MGMT-696X
Predictive Analytics
Using Social Media
ISYE-6180
Spring
CSCI-6390
Database Mining
Fall
CSCI-6968
Cryptography and Network
Security II
Spring
CSCI-4660
Networking 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
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.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)
data.rpi.edu (v0.1, 2009)
Soon…
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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!
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 ***
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.
And thus … in <10 years