Big Data and Education:
Education and Skills Development in Big
Data and Data Intensive Science

Yuri Demchenko
SNE Group, University of Amsterdam
ISO/IEC SGBD Big Data Technologies Workshop
Part of ISO/IEC Big Data Study Group meeting
13-16 May 2014

Introduction to discussion on Education and Training for
Big Data (or Data Intensive Science and Technologies)• Horizon2020: Education and skills development for Research eInfrastructure• Data Science education programs development by universities– Big Data and Data Science – Not only Data Mining and Data Analytics– Examples Data Science / Data Intensive Science / Big Data curricula
development• Initiatives and developments in US and EU– HPC University in US• Initiatives and developments at the University of Amsterdam (UvA)– Data Science Master program development– EDISON Initiative to coordinate Data Science curriculum
development

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Topics for Discussion• Big Data divide and need for Professional Education andTraining– Between scientific domains– Between IT and non-IT– Between countries• How to share experience between universities that havealready started development of Data Science programs?
– What experience do we have on component technologies?
– Existing instructional and educational concepts and technologies• Top down approach vs bottom up approach• Project based collaborative education• Education and Training program development approaches– Targeting different communities and research domains– Technical vs non-technical vs subject domains
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Horizon2020: Education and skills developmentfor Research e-Infrastructure (1)WP2014-2015 European research infrastructures (including e-Infrastructures)• EINFRA-4-2014 – Pan-European High Performance Computing infrastructureand services• EINFRA-5-2015 – Centres of Excellence for computing applications– … research in HPC applications; and addressing the skills gap in computational
science•

INFRASUPP-3-2014 – Strengthening the human capital of research
infrastructures– The skills and expertise specifically needed to construct, operate and use research
infrastructures successfully therefore are not widely available•

INFRASUPP-4-2015 – New professions and skills for e-infrastructures–

See next slide

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Horizon2020: Education and skills developmentfor Research e-Infrastructure (2)INFRASUPP-4-2015 – New professions and skills for e-infrastructures(1) Defining or updating university curricula for the e-infrastructure competences mentioned
above, and promoting their adoption.
(2) Developing and executing training programmes (including for lifelong learning) for theabove mentioned professionals working as part of a team of researchers or supporting
research teams.
(3) Support the establishment of these professions as distinct professions from that of a
researcher. Create a reference model which defines their competencies, supported bycase studies and best practices relating to e-infrastructures skills, human resourcesmanagement, support tools and related institutional practices. Develop alternatives means
for recognising non-research contributions by research technologists and data scientists.
(4) Support networking and information sharing among already practicing e-infrastructure
experts, research technologists, computation experts, data scientists and data librarians
working in research institutes and in higher education.
(5) Awareness raising activities; establish and promote e-infrastructures community
champions to advocate on new jobs and skills needs at schools, universities and scientificcommunities.

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Data Science education programs
development by universities•

Data Science programs in US and Europe – Info page
(Updated 5 Feb 2014) http://whatsthebigdata.com/2012/08/09/graduate-programs-in-big-data-and-data-science/

–
–
–
–

•

Indiana University (Prof. Geoffrey Fox)
San Diego Supercomputing Center (SDSC)
HPC University in US+
Many others

Currently most of programs are refactored/derived from Data Mining andAnalytics, Machine Learning, Business Analytics– Big Data and Data Science – Not only Data Mining and Data Analytics– Big Data require e-Infrastructure to support data processing, storing and management•

Examples Data Science / Data Intensive Science / Big Data curricula
development– University of Stavanger (UiS) and Purdue University– University of Liverpool and Laureate Online Education– University of Amsterdam and Vrij Univ Amsterdam•

Numerous initiatives to provide training to non-IT community– Librarians, Data archivists, etc.
– Data Intelligence 4 Librarians designed by 3TU.Datacentrum and DANS
http://dataintelligence.3tu.nl/en/home

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HPC University (HPCU) in US
HPC University is a Virtual Organization whose membership is open to allorganizations that would like to contribute to the preparation of current andfuture generations of HPC practitioners
http://hpcuniversity.org/• XSEDE, NCSA Blue Waters, Shodor Education Foundation, Inc., Ohio
Supercomputer Center, San Diego Supercomputer, ACM SIGHPC,
Cyprus Institute, Partnership for Advanced Computing in Europe(PRACE)• HPCU actively seeks participation from all sectors of the HPC communityto:– assess the learning and workforce development needs and requirements of thecommunity,– catalog, disseminate and promote peer-reviewed and persistent HPC resources,– develop new content to fill the gaps to address community needs,– broaden access by a larger and more diverse community via a variety of delivery
methods,– and pursue other activities as needed to address community needs

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Initiatives and Developments in EU• LERU (League of European Research Universities)– LERU Roadmap for Research Data
• Prepared by the LERU Research Data Working Group• Valuable contribution from LIBER, UKOLN• EGI (European Grid Initiative) Education and Training initiative• EUDAT Training program on Research Data and tools• EDISON Initiative (UvA, EGI, others)– Education for Data Intensive Science to Open New science frontiers• Initiatives from industry and commercial companies to support
Data Science and Big Data education– E.g. Engineering Group (HQ Italy) & Politecnico Turino, KPMG (NL) &UvA, Elsevier/LexisNexis (& UvA)

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EGI Partners to cooperate on Edu@Skills••••

National Centre for Scientific Research (CNRS, France)
INFN (Italy) and University of Catania
ENGINEERING (Italy) and Politecnico of Torino
Alliance Permanent Archives (APA) and APARSEN Project– FTK (Formal Education and Curricula)– GLOBIT (Online Continuous Professional Education Portal)– DANS (Education and Training Resources)– INMARK (Spreading Excellence)• University of Amsterdam (UvA)– Data Science Research Center– Academic Medical Center (AMC)Wsh SGBD, 13-16 May 2014

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Data Science Master at School of Mathematicaland Computer Sciences, Hariot Watt University

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Big Data Course at Laureate Online Education(University of Liverpool)
Seminar 1: Introduction. Big Data technology domain definition, Big Data Architecture
Framework
Seminar 2: Big Data use cases from Science, industry and business
Seminar 3: Architecture Framework for Big Data Ecosystem, Big Data Infrastructurecomponents
Seminar 4: Big Data analytic techniques, introduction to RapidMiner
Seminar 5: Understanding the processes behind Big Data
Seminar 6: Classification and forecasting techniques
Seminar 7: Big Data Security, Protection, and Privacy
Seminar 8: Integrating Big Data applications into enterprise IT infrastructure, data
regulation compliance

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Big Data Course at Laureate Online Education(University of Liverpool) - Details
Seminar 1: Introduction. Big Data technology domain definition, Big Data Architecture Framework•
Topics: Big Data and Data Intensive technologies definition; Big Data properties: Volume, Velocity, Variety, Variability,
Value, And Veracity. Big Data ecosystem, data origin, data target; raw data and actionable data.
Seminar 2: Big Data use cases from Science, industry and business•
Topics: Big data use cases analysis from science and industry: LHC in HEP, LOFAR/SKA in astronomy, genomic
research, Internet or web: target advertisement, recommender system (e.g. Netflix), Web Search, fraud detection
Seminar 3: Architecture Framework for Big Data Ecosystem, Big Data Infrastructure components•
Topics: Big Data Analytics Infrastructure and technology platforms, Enterprise Data Warehouses, MapReduce andHadoop, new file systems and database architectures, NoSQL.
Seminar 4: Big Data analytic techniques, introduction to RapidMiner•
Topics: Introduction to analytics and different analytical techniques. What are Analytics/Data Mining/Machine Learning?
Introduction to the RapidMiner toolkit. The use of simple pre-processing and Statistical techniques for modeling data will be
described and implemented using RapidMiner.
Seminar 5: Understanding the processes behind Big Data•
Topics: Introduction to Rule Extraction Algorithms and Cluster Analysis. Evaluating data from text streams. Decision tree
induction and Cluster Analysis techniques, use for enhancing business processes.
Seminar 6: Classification and forecasting techniques•
Topics: Machine Learning techniques, Neural Networks and Support Vector Machines, techniques used to analyze theperformance of our analytic processes. Measurement techniques such as Receiver Operating Curves and Gains Charts.
Seminar 7: Big Data Security, Protection, and Privacy•
Topics: Big Data security and data protection; data centric security models. Big Data privacy issues, differential privacy
and data re-identification.
Seminar 8: Integrating Big Data applications into enterprise IT infrastructure, data regulation compliance•
Topics: Enterprise business processes and data management issues; analysis, evaluation and optimisation; enterprisedata applications engineering. Data protection and corresponding standards, regulation and legislation; data provenance.
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EDISON Initiative
Data are becoming infrastructure themselves(Report “Riding the Wave”)• This requires large infrastructure resources to collect, store,
process and archive heterogeneous multi-faceted and linked
data.
• Data centric/data driven infrastructure has to support different
types of data, including text data, structured and unstructured
data, relational and vector data, linked data.
• Data appear in various contexts: large number strings fromexperiments or sensors, in software code, music, films,
publications, digital art, web pages, social media, public andbusiness statistics, and also orphan data.
• We need data scientists with the knowledge and skill to workwith existing and future data intensive infrastructure and tools.
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EDISON: Issues to address in academic training
•
•
•
•
•

Shape of the data infrastructure and its semantics
Data discovery, visualization, processing (filtering)
Infrastructure and capabilities for data analysis
Uses of data and its implications
Archiving, preservation, storage, permanency, data(de)selection, annotation• Trust: Quality and reliability. Tracking data evolution.
Curation. Safety.
• Legal, governance, environment, costs
Data scientists have to get familiar with the date universe andthe peculiarities of this landscape.
They will work in research, specific scientific data domains, andin the private sector.
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EDISON: Anticipated curriculum development• Create a cooperative community with universities thatoffer bachelor and master programs in Data Science,
Big Data and Data Archives.
• Consider the educational focus with a vision on thedeveloping data universe.
• Test and adapt educational programmes.
• Address implementation strategies with respect torequired expertise, methods, research training,
management and financial implications.
• Promote awareness and recognition (inform the market
sector; secure certification of new educational tracks).
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EDISON: Next steps• Formal establishment of the RDA Interest Group on
Education and Skills Development for Research
Data• Workshop – September 2014
– Adjacent to the next RDA4 meeting in Amsterdam• Information exchange: Website and billboard

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Data Scientist: New Profession and Opportunities
McKinsey Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp•

There will be a shortage of talent necessary for organizations to take advantage
of Big Data.
––

By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep
analytical skills as well as
1.5 million managers and analysts with the know-how to use the analysis of big data to makeeffective decisions

SOURCE:US Bureau of Labor Statistics; US Census; Dun & Bradstreet; company interviews; McKinsey analysis
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Strata Survey Skills and Data Scientist Self-ID

Analysing the Analysers. O’Reilly Strata
Survey – Harris, Murphy & Vaisman, 2013•
Based on how data scientists think
about themselves and their work•
Identified four Data Scientist clusters

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Skills and Self-ID Top Factors
Business
ML/BigData
Math/OR
Programming
Statistics

ML – Machine Learning
OR – Operations Research
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Slide from the presentation
Demystifying Data Science(by Natasha Balac, SDSC)

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Key to a Great Data Scientist
Technical skills (Coding, Statistics, Math)+ Commitment +Creativity+ Intuition+ Presentation Skills+ Business Savvy= Great Data Scientist!
• How Long Does It Take For a Beginner to Become a Good
Data Scientist?
– 3-5 years according to KDnuggets survey [278 votes total]

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Questions and Discussion

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Additional Information• Example Cloud Computing course development

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Common Body of Knowledge (CBK)in Cloud Computing
CBK refers to several domains or operational categories into which Cloud
Computing theory and practices breaks down•

Still in development but already piloted by some companies, including industry
certification program (e.g. IBM, AWS?)CBK Cloud Computing elements
1. Cloud Computing Architectures, service and deployment models
2. Cloud Computing platforms, software/middleware and API’s
3. Cloud Services Engineering, Cloud aware Services Design
4. Virtualisation technologies (Compute, Storage, Network)
5. Computer Networks, Software Defined Networks (SDN)
6. Service Computing, Web Services and Service Oriented Architecture (SOA)
7. Computing models: Grid, Distributed, Cluster Computing
8. Security Architecture and Models, Operational Security
9. IT Service Management, Business Continuity Planning (BCP)
10. Business and Operational Models, Compliance, Assurance, Certification
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CKB-Cloud Components LandscapeCloud Platforms, API

Cloud Architectures, Service
ModelsBusiness/Operational Models, Compliance, Assurance

Cloud Computing Fundamentals
Cloud Services Engineering&Design
DesignEngineering

Virtualisation

Networking

Web Services, SOA

Security, ID Management

Computing Models: Grid, Distributed, Cluster

IT Systems Management

Cloud Computing Common Body of Knowledge (Full)

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Multilayer Cloud Services Model (CSM) – Taxonomy of
Existing Cloud Architecture Models

Security InfrastructureManagement

Operations Support System

User/Client Services* Identity services (IDP)* Visualisation

User/Customer Side Functions and Resources

Administration andManagement Functions(Client)

Content/Data Services* Data * Content * Sensor * Device

1

Endpoint Functions* Service Gateway* Portal/Desktop

Access/Delivery
InfrastructureInter-cloud Functions* Registry and Discovery* Federation Infrastructure

SaaS

PaaS

Layer C5
Services
Access/Delivery

Layer C4Cloud Services(Infrastructure,
Platforms,
Applications,
Software)

Cloud Services (Infrastructure, Platform, Application, Software)

IaaS

Layer C6
User/Customer
side Functions

PaaS-IaaS IF
PaaS-IaaS Interface

IaaS – Virtualisation Platform Interface

Cloud Management
Software(Generic Functions)

Cloud Management Platforms
OpenNebula

Virtualisation Platform

OpenStack

KVM

VM

VM

Other
CMS

XEN

VMwareNetwork
Virtualis

Proxy (adaptors/containers) - Component Services and Resources

StorageResources

ComputeResources

Contrl&Mngnt Links

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VPN

Hardware/PhysicalResources

Network
Infrastructure

Layer C3
Virtual Resources
Composition andControl(Orchestration)

CSM layers(C6) User/Customer side
Functions(C5) Services
Access/Delivery(C4) Cloud Services(Infrastructure, Platform,
Applications)(C3) Virtual Resources
Composition andOrchestration(C2) Virtualisation Layer(C1) Hardware platform anddedicated network
infrastructure

Layer C2
Virtualisation

Layer C1
PhysicalHardwarePlatform andNetwork

Control/
Mngnt Links

Data Links

Data Links

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Relations Course Components and CSM

Layer C6
User/Customer
side Functions

User Applications

Layer C5
Services
Access/Delivery

Cloud Services API and Tools

Hybrid Clouds,
Integrated Infrastructure

User Applications

1

Cloud Service Design, Operational
proceduresManagement

Security

Operations Support System

Cross-layer
Service s (Planes)

Cloud/Intercloud Services Integration, Enterprise
IT infrastructure migration

1Customer Enterprise/Campus Infrastructure/Facility

Layer C4Cloud Services(Infrastructure,
Platforms,
Applications,
Software)Layer C3
Virtual Resources
Composition andControl(Orchestration)

Layer C2
Virtualisation

Cloud Services Platform

Cloud Service Provider
Datacenters and Infrastructure
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Layer C1
PhysicalHardwarePlatform andNetwork

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Professional Education in Cloud Computing Principles• Provide knowledge both in Cloud Computing as a newtechnology and background technologies• Empower the future professionals with ability to develop
new knowledge and build stronger expertise, prepare basis
for new emerging technologies such as Big Data
• Bloom’s Taxonomy as a basis for defining learning targets
and modules outcome• Provides a basis for knowledge testing and certification• Andragogy vs Pedagogy as instructional methodology for
professional education and training• Course format: On-campus education and training,
online courses, self-study
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Bloom’s Taxonomy – Cognitive Activities
Knowledge
Exhibit memory of previously learned materials by recalling facts, terms, basic concepts and answers••••

Knowledge of specifics - terminology, specific facts
Knowledge of ways and means of dealing with specifics - conventions, trends and sequences, classifications and categories, criteria, methodology
Knowledge of the universals and abstractions in a field - principles and generalizations, theories and structures
Questions like: What are the main benefits of outsourcing company’s IT services to cloud?

Comprehension
Demonstrate understanding of facts and ideas by organizing, comparing, translating, interpreting, describing, and stating the main ideas••

Translation, Interpretation, Extrapolation
Questions like: Compare the business and operational models of private clouds and hybrid clouds.

Application
Using new knowledge. Solve problems in new situations by applying acquired knowledge, facts, techniques and rules in a different way•

Questions like: Which cloud service model is best suited for medium size software development company, and why?

AnalysisExamine and break information into parts by identifying motives or causes. Make inferences and find evidence to support generalizations••

Analysis of elements, relationships, organizational principles
Questions like: What cloud services are needed to support typical business processes of a web trading company? Give suggestions
how these services can be implemented with PaaS or IaaS clouds. Provide references to support your statements.

Synthesis
Compile information together in a different way by combining elements in a new pattern or proposing alternative solutions••

Production of a unique communication, a plan, or proposed set of operations, derivation of a set of abstract relations
Questions like: Describe the main steps and tasks for migrating IT services of an example company to clouds? What services and data
can be moved to clouds and which will remain at the enterprise premises.

Evaluation
Present and defend opinions by making judgments about information, validity of ideas or quality of work based on a set of criteria••

Judgments in terms of internal evidence or external criteria
Questions like: Do you think that cloudification of the enterprise infrastructure creates benefits for enterprises, short term and longterm?

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Mapping Bloom’s Taxonomy from Cognitive Domain
to Professional Activity Domain
•
•

•

•
•

Perform standard tasks,
use API and Guidelines
Create own complex
applications using
standard API (simple
engineering)
Integrate different
systems/components,
e.g. Cloud provider andenterprise (complex
engineering)
Extend existing services,
design new services
Develop newarchitecture and models,
platforms andinfrastructures

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Remember(Knowledge)
Understand(Comprehension)
Apply(Application)

Analyse(Analysis)

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Evaluate(Evaluation)

Create(Synthesis)30

Mapping Course Components, Cloud Professional
Activity and Bloom’s Taxonomy

Hybrid Clouds, Integrated
Infrastructure

Cloud Services
API and Tools

User Applications

User Applications

1

Cloud/Intercloud Services Integration, Enterprise IT
infrastructure migration

Cloud Service Design, Operational procedures

Customer Enterprise/Campus Infrastructure/Facility

Cloud Services Platform

Cloud Service Provider
Datacenters and Infrastructure

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Pedagogy vs Andragogy
Pedagogy (child-leading) and Andragogy (man-leading)• On-campus and on-line education• Developed by American educator Malcolm Knowles, statedwith six assumptions related to motivation of adult learning:– Adults need to know the reason for learning something (Need to
Know)– Experience (including error) provides the basis for learning activities(Foundation)– Adults need to be responsible for their decisions on education;
involvement in the planning and evaluation of their instruction (Selfconcept)– Adults are most interested in learning subjects having immediate
relevance to their work and/or personal lives (Readiness).
– Adult learning is problem-centered rather than content-oriented(Orientation)– Adults respond better to internal versus external motivators(Motivation)
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Applying Andragogy to Self-Education and Online
Training - Problems• Andragogy concept is widely used in on-line educationbut– Based on active discussion activities guided/moderated byinstructor/moderator– Combined with the Bloom’s taxonomy• Self-education (guided) and online training specifics– Course consistency in sense of style, presentation/graphics,etc– Requires the course workflow to be maximum automated• Especially if coupled with certification or pre-certification– Less time to be devoted by trainee• Estimated 1 hour per lesson, maximum 3 lessons per topic– Knowledge control questionnaires at the end of lessons or
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