EDISON: Coordination and cooperation to establish new
profession of Data Scientist for European Research andIndustry

Yuri Demchenko
University of Amsterdam
Data and computing infrastructures for open
scholarship Workshop
22 September 2015, RDA6, ParisEDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Outline• Consortium members• EDISON Project Objectives• Project structure – WPs, Tasks and main products• EDISON Approach:– eCFv3.0 and CF-DS definition– DS-BoK and Model Curriculum– Education and Training Infrastructure for Data Science• EDISON and e-Infrastructure for Open Scholarship– Today’s questions - Topics for discussion

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EDISON Consortium MembersUniversities
1. University of Amsterdam (UvA) - Coordinator
2. University of Stavanger (UiS)
3. University of Southampton (UK)Community related partners
4. European Grid Initiative (EGI)
5. FTK – Research Institute for Telecommunication and Cooperation (DE)and APARSEN (Alliance Permanent Access to Records of Science inEuropean Network)

Industry partner
6. Industry: Engineering (Italy)

SME
7. InMark (Spain)
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Project Objectives
Objective 1: Promote the creation of Data Scientists curricula by an increasing numberof universities and professional training organisations.
Define common framework for building new curricula on Data Science including Data Science
Competence Framework (CF-DS), Body of Knowledge (DS-BoK) and Model Curriculum (MC-DS).
Work with champion universities and organisations to implement MC-DS

Objective 2: Provide conditions and environment for re-skilling and certifying Data
Scientists expertise to graduates, practitioners and researchers throughout theircareers.
Support Data Science education and training for students and “self-made” Data Science practitioners toallow their formal professional certification.
Create EDISON Education and Training Marketplace by leveraging EGI Engage Training Marketplace.

Objective 3: Develop a sustainable business model and a roadmap to achieve a highdegree of competitiveness for European education and training on Data Science
technologies, provide a basis for the formal recognition of the Data Scientist as a newprofession.
Create Community of practice for sustainable Data Science education and training supported by
EDISON Liaison Group(s).

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4Objectives 1: Data Science Profession Definition:
Competences Framework and Model Curriculum
Objective 1: Promote the creation of Data Scientists curricula by an increasing numberof universities and professional training organisations.
EDISON contribution• The Data Science Competence Framework (CF-DS) including Glossary and Taxonomy ofcompetences and skills.
• The Body of Knowledge (BoK) for the Data Scientist Professional (DS-BoK) that will be
used to map required competencies/skills and existing academic, research and technology
disciplines• A Model Curriculum for Data Science (MC-DS) that will provide a reference
implementation of the proposed CF-DS and DS-BoK.

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Objective 2: Education and Training Environment
Objective 2: Provide conditions and environment for re-skilling and certifying Data
Scientists expertise to graduates, practitioners and researchers throughout theircareers.
Provide conditions for a sustainable increase in the number of Data Scientists in Europe tosatisfy the level of research and industry demand in coming 5-8 years.
• Education and Training e-Infrastructures to support specialist training on Data Sciencethat will include access to data sets for educational purposes, data management andanalytical tools and cloud based virtual labs and classrooms.
• EDISON Marketplace of existing and developing education and training courses, educationmaterials and other resources, by leveraging on the EGI Knowledge Commons and theTraining Market initiative.
• A model and a framework for Data Science education programme accreditation andprofessional certification.

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Objective 3: Sustainability model
Objective 3: Develop a sustainable business model and a roadmap to achieve a highdegree of competitiveness for European education and training on Data Science
technologies, provide a basis for the formal recognition of the Data Scientist as a newprofession.
• An education and training model that will include multiple learning models (e.g.
residential, lifelong learning, e-Learning) that will allow adjusting individual career path inData Science.
• A business model that incorporates the proposed education model into a sustainable cycleof the research data value chain including the major stakeholders and actors from research,industry and government.
• A roadmap for a sustainable education and training services as a part of overall data
driven technologies development in Europe.

•EDISON Liaison Group(s) (ELG) of independent experts that represent the major
stakeholders in Data Science that will work as a consulting body for the project and will
create a basis for future independent expert group for universities and for EC.

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Basic methodology of EDISON, inputs and work
packages

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WPs interaction and main tasks
Education and Training Environment

EDISON Activity Links
Community and Feedback Links

WP3 Development andImplementation Strategy

WP4 Sustainability andcertification of the Data
Scientist Profession

Data Scientist Model
Curricula&Profiles (MC-DS)

WP2 Educational Focus
and DS-BoKBusiness Model
Edu&Train Models(Residential, On-Line, LifeLong Learning)

Data Scientist Competences
Framework (CF-DS)

Body of Knowledge Data
Science (BoK-DS)

Sustainability Model andImplementation Plan

Data Science Accreditation
Framework

Edu&Train Resr Inventory
and Taxonomy (ETR-Tax)

Data Scientist
Certification Scheme
Plan

Education & Training
Marketplace

Edu&Train Resources
Directory (ETR-Dir)

Education and Training
Model Roadmap

Education & Training
Environment and Infrastruct

Education & Training
Infrastructure
WP5 Dissemination and Engagement
EDISON Liaison
Groups(Univ, Employers,Experts)

Promotional anddissemination material

Studs/Teachers
Studs/Teachers

Champion Organisations
And Pioneer Universities

Infrastr for Open ScholarshipStuds/Teachers

Self-made Data
Scientists

Target community: Students,Researchers, Practitioners,
ICT staff

Universities and Professional
Training Organisations

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9EDISON Approach (1): Data Science Definition• Data Science and Data Scientist Professional (DSP) definition– Including organizational roles and general competence domains– General Data Science literacy – common knowledge to use Data Sciencetools and specify tasks for DSP/engineers

Definition by NIST Big Data WG (20142015)
A Data Scientist is a practitioner who
has sufficient knowledge in theoverlapping regimes of expertise inbusiness needs, domain knowledge,
analytical skills, and programming andsystems engineering expertise tomanage the end-to-end scientificmethod process through each stage inthe big data lifecycle.
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EDISON Approach (2): e-CFv3.0 and CF-DS•Competence Framework for Data Science (CF-DS) definition will be built based on
European e-Competence framework for IT (e-CFv3.0)– Linking scientific research lifecycle, organizational roles, competences, skills and knowledge– Defining Data Science Body of Knowledge (DS-BoK)– Mapping CF-DS and DS-BoK to academic disciplines• Multiple use of e-CFv3.0 within ICTorganisations• Provides basis for individual career
path, competence assessment, training
and certification• EDISON CF-DS will be used for
defining DS-BoK and MC-DS, linking
organizational functions and requiredknowledge• Provide basis for individual (self)
training and certification

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11e-CFv3.0 Internal Structure: Refactoring for CF-DS• s• 4 Dimensions– Competence Areas– Competences– Proficiency levels– Skills and Knowledge• 5 Competence Area defined by ICTBusiness Process stages– Plan– Build– Deploy– Run– Manage-> Refactor to Scientific Research (orScientific Data) Lifecycle– See example of RI manager at IG-ETRD wikiand meeting• Each competence has 5 proficiency level– Ranging from technical to engineering tomanagement to strategist/expert level• Knowledge and skills property are definedfor/by each competence and proficiency
level (not unique)

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EDISON Difference – Long Term Perspective• Main project results are targeted for “durable” use and future effect– Education and Data Science curriculum innovation to take effect in 3-5 years– Training and re-skilling to solve short-term needs– Sustainability model and stakeholder involvement• Propose multiple paths for Data Science career– Historical career cycle was 10-15 years– Now 5-7 year in current conditions of accelerated technologies change– Need to understand trends• USA PCAST report (August 2015) on Networking and Information Technology R&D(NITRD) stresses the importance of education and governmental support [ref]• Community involvement and contribution– EDISON Liaison Groups of experts– Champion universities and organisations[ref] https://www.whitehouse.gov/blog/2015/08/07/pcast-assesses-federal-information-technology-rd
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Topics for Discussion
e-Infrastructures for Open Scholarship: Interoperability andEcosystem/Sustainability and Advancement•How to deconstruct Open Scholarship into basic ideas/elements and e-infra-matching elements? Thegoal is to agree on terminology, goals and objectives, and perhaps more importantly priorities.

(1)
(2)
(3)
•How can e-Infrastructures facilitate Open Scholarship? The focus should be on services (including
training and support), costs, incentives for use, barriers and how to go about solving them.

(1)(2)•Terminology is important, especially in new areas such as Data Science or Big Data
Define goal and expected outcome – future vision and long term trends
Innovation from Science to Industry – closer relations with industry, also as apossible career path for researchers (and Data Scientists in particular)

Address human factor: Education and Training infrastructure as a part of and linked
to e-Infrastructure
Education and training linked to Scientific research lifecycle (also Research Datalifecycle) and organizational roles

Open Scholarship requires transparency at all levels of the research life-cycle, which effectively leads totrust and uptake. What/who do we need to mobilize the right stakeholders to build this trust?

(1)
(2)
(3)
(4)

Trustworthiness (trust by design) and data-centric security model
Federated infrastructure – to increase trust by collective management of policies
Privacy and Opacity (as opposite to transparency)
Ethics and Responsible use: Data based economy == Resource based economy

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