Date: 2015-12-11 Presenter: Eun-Kyeong Kim (eun-kyeong.kim@psu.edu) Symposium: The 17th KOCSEA (Korean Computer Scientists and Engineers Association in America) Technical Symposium 2015

1. Building a Big Data Analytics
Workforce in iSchools
Penn State Big Data Education Project Team
Presenter: Eun-Kyeong Kim (Ph.D. Candidate)
(eun-kyeong.kim@psu.edu)
The GeoVISTA Center, The Department of Geography
The Pennsylvania State University
KOCSEA 2015
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2. Big Data Education Project Team
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Dr. Jungwoo Ryoo
Associate Professor,
IST at Penn State Altoona
PI Co-PIs
Dr. Soo-yong Byun
Associate Professor
Education at Penn State
University Park
Dr. Dongwon Lee
Associate Professor
IST at Penn State University
Park
Graduate Project Manager
M.S. Eun-Kyeong Kim
Ph.D. Candidate,
Geography (GIScience) at Penn
State University Park
Undergraduate Research Associates
William Aiken
Security and Risk Analysis
Penn State Altoona Penn State University Park
Whitney Hernandez Victoria McIntyre
Computer Science
Ryan A. Bury
Geography (GIS)
Nate Gould William Casselberry

3. Table of Content
• Why does Big Data Education matter?
• NSF-sponsored project: Big Data Education
– Goals & objectives
– Project team & timelines
– Learning module 1, 2, 3 for big data analytics
– Deliverables & workshops
• Call for Participations
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4. Buzzword: Big Data
Everyone talks about Big Data in these days.
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5. Why: the Explosion of Data
• Data grows exponentially fast in volume
and variety.
– SDSS (the Sloan Digital Sky Survey):
about 200 GB / day.
– LSST (Large Synoptic Survey Telescope):
about 140 TB / 5 days.
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6. Why: Big Data is useful
• Many applications of big data analytics
• The U.S. government “Big Data Research and
Development Initiative” in 2012
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7. Why: Demand in Manpower
• McKinsey,“The United States alone faces a
shortage of 140,000 to 190,000 people with
analytical expertise and 1.5 million
managers and analysts with the skills to
understand and make decisions based on
the analysis of big data.”
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8. The Current State of Big Data Education
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Course Title Offered by
Building a Data Science Team Johns Hopkins via Coursera
Data Analysis and Statistical Inference Duke via Coursera
Mining Massive Data Sets Stanford via Coursera
Course Title
Techniques and Concepts of Big Data
Hadoop Fundamentals
Up and Running with Public Data Sets
William Aiken. (2015). Online Courses on Big Data Analytics. http://sites.psu.edu/bigdata/2015/11/18/online-courses-on-big-data-analytics/
23 Great Schools with Master’s Programs in Data Science. http://www.mastersindatascience.org/schools/23-great-schools-with-masters-programs-in-data-science/
MS in Business Analytics &
Information Management
MS in Analytics
Offline Curricular Online Courses

9. The Current State of Big Data Education
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Course Title Offered by
Building a Data Science Team Johns Hopkins via Coursera
Data Analysis and Statistical Inference Duke via Coursera
Mining Massive Data Sets Stanford via Coursera
Course Title
Techniques and Concepts of Big Data
Hadoop Fundamentals
Up and Running with Public Data Sets
William Aiken. (2015). Online Courses on Big Data Analytics. http://sites.psu.edu/bigdata/2015/11/18/online-courses-on-big-data-analytics/
23 Great Schools with Master’s Programs in Data Science. http://www.mastersindatascience.org/schools/23-great-schools-with-masters-programs-in-data-science/
MS in Business Analytics &
Information Management
MS in Analytics
Offline Curricular Online Courses

10. Big Data Education for iSchools
• Interdisciplinary institutions addressing
broad “information”-related problems
• 65 world-wide institutions
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11. Big Data Education for iSchools
• Interdisciplinary institutions addressing
broad “information”-related problems
• 65 world-wide institutions
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12. Big Data Education for iSchools
• Interdisciplinary institutions addressing
broad “information”-related problems
• 65 world-wide institutions
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13. NSF-funded Research Project:
Building a Big Data Analytics
Workforce in iSchools
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14. Building a Big Data Analytics
Workforce in iSchools
• In this project, our team …
1) Develop three types of learning modules to
teach big data analytics to undergraduates
in iSchools;
2) Develop faculty expertise for teaching the
developed materials;
3) Implement the learning modules and
evaluate students’ learning.
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15. Objectives
More concretely, we …
(1) Develop, assess, and disseminate three
innovative learning modules;
(2) Prepare faculty with pedagogical guidelines
and lesson plans;
(3) Institutionalize the learning modules and
teaching strategies among a community of 17
iSchool campuses at Penn State & beyond;
(4) Disseminate the developed materials and
practices into wider audience.
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16. Big Data Education Project Team (1/3)
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Dr. Jungwoo Ryoo
Associate Professor,
IST at Penn State Altoona
PI Co-PIs
Dr. Soo-yong Byun
Associate Professor
Education at Penn State
University Park
Dr. Dongwon Lee
Associate Professor
IST at Penn State
University Park
Graduate Project Manager
M.S. Eun-Kyeong Kim
Ph.D. Candidate,
Geography (GIScience) at Penn
State University Park
Undergraduate Research Associates
William Aiken
Security and Risk Analysis
Penn State Altoona Penn State University Park
Whitney Hernandez Victoria McIntyre
Computer Science
Ryan A. Bury
Geography (GIS)
Nate Gould William Casselberry

17. Big Data Education Project Team (2/3)
17
Advisory Board Members
Alan MacEachren, Ph.D.
The Director, The GeoVISTA Center
Professor,The Dept. of Geography
at Penn State University Park
David Fusco, Ph.D.
Lecturer, IST
at Penn State University Park
David Fusco, Ph.D.
Professor, IST
at Penn State University Park
Jeongkyu Lee, Ph.D.
Associate Professor, The Dept.
of CSE
at University of Bridgeport
Jongwook Woo, Ph.D.
Professor,The Dept. of Computer
Information Systems
at California State University,
Los Angeles
Marlies Temper, M.A.
Senior Researcher, The Dept. of
Computer Science and Security
Institute of IT Security Research
Simon Tjoa, M.A.
FH lecturer & International
Coordinator, The Dept. of
Computer Science and Security
Institute for IT Security Research
William Cantor, Ph.D.
Senior Instructor, IST
at Penn State York

18. Big Data Education Project Team (3/3)
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Collaborating Institutions
Internal Collaborator
Penn State Berks
External Collaborator
George Mason University
iSchool Collaborators
Drexel University The University of Pittsburgh
2-yr-college Collaborators
YTI Career Institute South Hills

19. Task 1: Learning Modules
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• Learning modules used for 2-3 weeks in one
semester
• Module 1: Digital Storytelling about Big Data
– Using “storytelling” as an education tool to building
awareness about big data, big data analytics
techniques, and big data-related career opportunity
• Module 2: Security Analysis in the Cloud
• Module 3: Big Data Mining

20. Task 1: Learning Modules
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• Module 2: Security Analysis in the Cloud
– About how big data analytics can be used to
address challenges in various IT domains (e.g.
network security, sensor networks, and
human/device-generated signals).
• Module 3: Big Data Mining
– About how big data analytics is used to solve
real-life problems in data mining applications
(e.g. online dating site, climate change, and
infectious disease research using social media).

21. Task 2 & 3
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• Task 2: Implementing Learning modules and
Developing Faculty Expertise
• Task 3: Evaluating Educational Innovations
– Using pre-tests and posttests
– Control groups (traditional methods)
vs. Target groups (innovative methods)

22. Timeline (2015.09 – 2018.08)
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Year 1
Year 2

23. 23
Year 3
Timeline (2015.09 – 2018.08)

24. Deliverables (1/2) – Big Data E-Textbook
• Co-Authors: Jungwoo Ryoo, Eun-Kyeong Kim
• Authors are not limited to the project team.
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25. Deliverables (1/2) – Big Data E-Textbook
• Co-Authors: Jungwoo Ryoo, Eun-Kyeong Kim
• Authors are not limited to the project team.
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Teaching materials & guidelines
for faculty & students

26. Deliverables (2/2) –Blog Entries &
Publications
• Blog Entries
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27. Deliverables (2/2) –Blog Entries &
Publications
• Blog Entries
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28. Deliverables (2/2) –Blog Entries &
Publications
• Blog Entries
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29. Call for Participations
• Join our research project as a community
member!
• http://sites.psu.edu/bigdata/community/
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@BigData_Edu
BigData.Edu.Proj@gmail.com
http://sites.psu.edu/BigData

30. Thank you for attending! 
@BigData_Edu
BigData.Edu.Proj@gmail.com
http://sites.psu.edu/BigData
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