EDISON Project Overview:

Activities, developments and products toestablish the Data Science profession
Yuri Demchenko, EDISON Project
University of Amsterdam
3rd EDISON Champion Universities
Conference
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

19-20 June 2017, Warsaw

Outline•

Background– Recent EU Initiatives, European Digital Single Market (DSM) and demand for data
enabled skills– Challenges with growing demand and gap for Data Science competences and skills•

EDISON Data Science Framework (EDSF)– From Data Science Competences and Skills to Body of Knowledge and Model
Curriculum– Data Science Professional Profiles family and organisational skills management•••

Use of EDSF for Data Science curricula design and skills management
Further activities and sustainability
Summary and discussion

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Visionaries and Drivers:
Seminal works, High level reports, Activities
The Fourth Paradigm: Data-Intensive Scientific Discovery.
By Jim Gray, Microsoft, 2009. Edited by Tony Hey, Kristin Tolle, et al.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
Riding the wave: How Europe can gain fromthe rising tide of scientific data.
Final report of the High Level Expert Group on
Scientific Data. October 2010.
http://cordis.europa.eu/fp7/ict/einfrastructure/docs/hlg-sdi-report.pdf

The Data Harvest: Howsharing research data
can yield knowledge,
jobs and growth.
An RDA Europe Report.
December 2014

https://www.rd-alliance.org/
HLEG report on European
Open Science Cloud(October 2016)

EDISON Warsaw June 2017https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

Emergence of Cognitive Technologies(IBM Watson and others)
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The Fourth Paradigm of Scientific Research
1.
2.

Theory, hypothesis and logical reasoning
Observation or Experiment––

3.

Simulation of theory or model–

4.

E.g. Newton observed apples falling to design his theory of mechanics
But Gallileo Galilei made experiments with falling objects from the Pisa
leaning tower
Digital simulation can prove theory or model

Data-driven Scientific Discovery (aka Data Science)– More data beat hypnotized theory– e-Science as computing and Information Technologies empowered science

5. Computer-human- driven science?
–

Machine discovers new patterns and formulates hypothesis in one or
multiples knowledge spaces

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Recent European Commission Initiatives 2016
Digitalising European Industry: Reaping the full benefits of a Digital Single
Market. COM(2016) 180 final, Brussels, 19.4.2016•

The need for new multidisciplinary and digital skills in particular Data Scientist–

Expected rapidly growing demand will lead to more than 800 000 unfilled vacancies by 2020

A New Skills Agenda for Europe, COM(2016) 381 final Brussels, 10.6.2016•

Launch Digital Skills and Jobs Coalition (1st December 2016, Brussels) to develop
comprehensive national digital skills strategies by mid-2017

European Cloud Initiative - Building a competitive data and knowledge economy inEurope, COM(2016) 178 final, Brussels, 19.4.2016•

European Open Science Cloud (EOSC) and European digital research and data
infrastructure–

To offer 1.7 million European researchers and 70 million professionals in science and technology
open and seamless services for storage, management, analysis and re-use of research data

HLEG report on European Open Science Cloud (October 2016) identified need fordata experts and data stewards••

Estimation: More than 80,000 data stewards (1 per every 20 scientists)
Core Data Experts need to be trained and their career perspective improved

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Industry reports on Data Science Analytics and Dataenabled skills demand•

Final Report on European Data Market Study by IDC (Feb 2017)– The EU data market in 2016 estimated EUR 60 Bln (growth 9.5% fromEUR 54.3 Bln in 2015)• Estimated EUR 106 Bln in 2020
– Number of data workers 6.1 mln (2016) - increase 2.6% from 2015
• Estimated EUR 10.4 million in 2020
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply estimated 769,000 (2020) or 9.8%•

PwC and BHEF report “Investing in America’s data science andanalytics talent: The case for action” (April 2017)
–
–
–

•

http://www.bhef.com/publications/investing-americas-data-science-andanalytics-talent
2.5 mln postings, 23% Data Scientist, 67% DSA enabled jobs
DSA enabled jobs growing at higher rate than main Data Science jobs

Citing EDISON and EDSF

Burning Glass Technology, IBM, and BHEF report “The Quant
Crunch: How the demand for Data Science Skills is disrupting the
job Market” (April 2017)––https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576
USEN.PDF
DSA enabled jobs takes 45-58 days to fill: 5 days longer than average
Influenced by EDISONEDISON Warsaw June 2017

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ICT and Data related Skills shortage - Impact• Problems with hiring (skilled) ICT related staff– At least one year for training and acquiring experience– As soon as new employees are confident with their skills, they leave forbig(ger) companies or industry• Open Data Science/Stewards positions stay unfilled longer– In research institutions for months and years– In industry for months• Companies/organisations want experienced Data Science workers– There is no time to acquire necessary experience• Millennials factor– Do we understand difference of the millennials workforce?

• Challenges: How to obtain, train in shorter period and sustain new
digital (ICT and Data related) skills in organisations
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Sustainable ICT and Data Skills Development• HLEG report on EOSC rose question about critical need for Core Data
Experts– Not much changes since report publication in October 2016
– Some minor disconnected plans for future H2020 WP2018-2020• Educate vs Train– Training is a short term solution– Education is a basis for sustainable skills development• Technology focus changes every 3-4 years– Study: 50% of academic curricula are outdated at the time of graduation• Lack of necessary skills leads to underperforming projects andorganisations and loose of competitiveness– Challenge: Policy and decision makers don’t have mind set to plan human factor(competences and skills) as a part of technology strategy• Need to change skills management paradigm– Dynamic (self-) re-skilling: Continuous professional development and shared
responsibility between employer and employee– Skills and career management as a part of professional orientation

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EDISON Products for Data Science Skills
Management and Tailored Education•

EDISON Data Science Framework (EDSF)– Compliant with EU standards on competences andprofessional occupations e-CFv3.0, ESCO
– Customisable courses design for targeted education andtraining•

Skills development and career management for Core
Data Experts and related data handling professions•

Capacity building and Data Science team design•

Academic programmes and professional training
courses (self) assessment and design•

EU network of Champion universities pioneering Data
Science academic programmes•

Engagement in relevant RDA activities and groups•

Cooperation with International professional organisations
IEEE, ACM, BHEF, APEC (AP Economic Cooperation )EDISON Warsaw June 2017

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EDISON Data Science Framework (EDSF)

DS-BoK

MC-DS

EDISON Online
Educational Environ

CF-DS
Taxonomy andVocabulary

Data Science
Framework

Edu&Train Marketpltz
and Directory

DS Prof Profiles

Foundation & Concepts

Services

Roadmap &Sustainability• Community
Portal (CP)• Professional
certification• Data Science
career & prof
development

Biz Model

EDISON Framework components

Methodology

–
–
–
–
–

•

ESDF development based on job market
study, existing practices in academic,
research and industry.

•

Review and feedback from the ELG, expert
community, domain experts.

•

Input from the champion universities andcommunity of practice.

–

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP – Data Science Professional profiles
Data Science Taxonomies and Scientific Disciplines
Classification
EOEE - EDISON Online Education EnvironmentEDSF Background: Standards and Best Practices•

e-CFv3.0 - European e-Competence Framework for IT–•

CWA 16458 (2012): European ICT Professional Profiles Family Tree–•

Structured by 4 Dimensions and organizational processes• Competence Areas: Plan – Build – Run – Enable - Manage• Competences: total defined 40 competences• Proficiency levels: identified 5 levels linked to professional education levels• Skills and Knowledge
Defines 23 ICT profiles for common ICT jobs

ESCO (European Skills, Competences, Qualifications and Occupations) framework––Standard for European job market since 2016
Expected inclusion of the Data Science occupations family – end 2017••

ACM Classification of Computer Science – CCS (2012)
ACM Computer Science Body of Knowledge (CS-BoK) and ACM and IEEE Computer
Science Curricula 2013 (CS2013)•

NIST SP 1500-1 NIST Big Data interoperability Framework (NBDIF): Volume 1: Definitions,
September 2015

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Data Scientist definition
Based on the definitions by NIST Big Data WG (NIST SP1500 - 2015)•

A Data Scientist is a practitioner who has sufficientknowledge in the overlapping regimes of expertise inbusiness needs, domain knowledge, analytical skills,
and programming and systems engineering expertise
to manage the end-to-end scientific method process
through each stage in the big data lifecycle– … Till the delivery of an expected scientific and businessvalue to science or industry•

Profession is defined via Competences mapped to[ref] Legacy: NIST BDWG
definition of Data Science– Skills and Knowledge– Proficiency levels••

Data science is the empirical synthesis of actionable knowledge and technologies required tohandle data from raw data through the complete data lifecycle process.
Big Data is the technology to build system and infrastructures to process large volume ofstructurally complex data in a time effective way

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Identified Data Science Competence Groups•

Core Data Science competences/skills groups– Data Science Analytics (including Statistical Analysis, Machine Learning, Business
Analytics)– Data Science Engineering (including Software and Applications Engineering, Data
Warehousing, Big Data Infrastructure and Tools)– Domain Knowledge and Expertise (Subject/Scientific domain related)•

EDISON identified 5 core competence groups demandedby organisations– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

Other skills commonly recognized aka “soft skills” or personal– 21st Century Skills – required to effectively work in the modern agile organisations– Data Science professional skills: Thinking and acting like Data Scientist – required tosuccessfully develop as a Data Scientist and work in Data Science teams

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Data Science Competence Groups - Research
Data Science Competences
include 5 groups
•
•
•
•
•

Data Science Analytics
Data Science Engineering
Domain Knowledge and Expertise
Data Management
Scientific Methods or Business
Process Management

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesis Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design

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Data Science Competences Groups – Business
Data Science Competences
include 5 groups
•
•
•
•
•

Data Science Analytics
Data Science Engineering
Domain Knowledge and Expertise
Data Management
Scientific Methods or Business
Process Management

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis

Business Process
Operations/Stages
•
•
•
•
•
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DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design15

Identified Data Science Competence Groups
Data Science Analytics Data Management(DSDA)(DSDM)

Data Science Engineering Research/Scientific Methods(DSENG)(DSRM)

Data Science Domain
Knowledge, e.g. Business
Processes (DSDK/DSBPM)
Create new understandings andUse domain knowledge
capabilities by using the scientific (scientific or business) tomethod (hypothesis,
develop relevant data analytics
test/artefact, evaluation) orapplications, and adopt generalsimilar engineering methods toData Science methods todiscover new approaches todomain specific data types andcreate new knowledge andpresentations, data and processachieve research ormodels, organisational roles
organisational goals
and relations

0

Use appropriate
statistical techniques
and predictive
analytics on available
data to deliverinsights and discover
new relations

Develop andimplement data
management
strategy for datacollection, storage,
preservation, andavailability forfurther processing.

Use engineering
principles to research,
design, develop andimplement new
instruments andapplications for datacollection, analysis andmanagement1

DSDA01
Use predictive
analytics to analyse big
data and discover new
relations2

DSDA02
Use statistical techniq
to deliver insights

DSDM01Develop andimplement data
strategy, in particular,
Data Management
Plan (DMP)DSDM02Develop data models
including metadata

DSENG01
Use engineering principles
to design, prototype data
analytics applications, ordevelop instruments,
systems
DSENG02
Develop and apply
computational solutions

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

4

DSDA04
Analyze complex data

5

DSDA05Use different analytics

DSRM01Create new understandings andcapabilities by using scientific/
research methods or similar domain
related development methods

DSBPM01
Understand business and provide
insight, translate unstructured
business problems into an abstract
mathematical framework

DSBPM02
Participate strategically andtactically in financial decisions

DSENG03
Develops specialized tools

DSRM02
Direct systematic study toward afuller knowledge or understandingof the observable facts
DSRM03
Undertakes creative work

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

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DSBPM03
Provides support services to other16

Identified Data Science Skills/Experience Groups•

Group 1: Skills/experience related to competences
–
–
–
–
–
–

•

Group 2: Big Data (Data Science) tools and platforms
–
–
–
–
–
–

•

Big Data Analytics platforms
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools

Group 3: Programming and programming languages and IDE–•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data
management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
Application/subject domain related (research or business)
Mathematics and Statistics

General and specialized development platforms for data analysis and statistics

Group 4: Soft skills or 21st Century Skills–

Personal, inter-personal communication, team work, professional network

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Key Data Science Analytics Competencesby EDISON and DARE Project for APEC countries• Core/foundational competences (starting from entry level to expert level)– Statistics, Probability theory, mathematics, calculus– Statistical programming languages, frameworks, tools– Computational methods and document processing tools (including Excel,
Office visualization, or similar)– Data Visualisation, and tools (e.g. Tableau, SPSS)• Data Science Analytics (including Data Mining, Machine Learning)– Extended (data driven technologies): Optimization, simulation, etc.

• Data Science Engineering– Including applications development, Big Data Infrastructure design and operation, Data
Warehousing, Data and infrastructure Security• Research methods and Business process methods• Domain related knowledge (e.g. scientific domains, business, industry,
public sector)• 21st Century Skills
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21st Century Skills (DARE & BHEF & EDISON)
1.

Critical Thinking: Demonstrating the ability to apply critical thinking skills to solve
problems and make effective decisions
2. Communication: Understanding and communicating ideas
3. Collaboration: Working with other, appreciation of multicultural difference
4. Creativity and Attitude: Deliver high quality work and focus on final result, intitiative,
intellectual risk
5. Planning & Organizing: Planning and prioritizing work to manage time effectively andaccomplish assigned tasks
6. Business Fundamentals: Having fundamental knowledge of the organization and theindustry
7. Customer Focus: Actively look for ways to identify market demands and meet customer
or client needs
8. Working with Tools & Technology: Selecting, using, and maintaining tools andtechnology to facilitate work activity
9. Dynamic (self-) re-skilling: Continuously monitor individual knowledge and skills as
shared responsibility between employer and employee, ability to adopt to changes
10. Professional networking: Involvement and contribution to professional network activities
11. Ethics: Adhere to high ethical and professional norms, responsible use of power data
driven technologies, avoid and disregard un-ethical use of technologies and biased datacollection and presentation

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Data Science Soft Skills:
Thinking and Acting like Data Scientist
1.
2.
3.
4.
5.
6.
7.

8.
9.

10.
11.
12.
13.
14.

15.

Accept/be ready for iterative development, know when to stop, comfortable with falure, accept thesymmetry of outcome (both positive and negative results are valuable)
Ask the right questions
Recognise what things are important and what things are not important
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
Recognise value of data, work with raw data, exercise good data intuition
Good sense of metrics, understand importance of the results validation, never stop looking at
individual examples
Be aware about power and limitations of the main machine learning and data analytics algorithms
and tools
Understand that most of data analytics algorithms are statistics and probability based, so any
answer or solution has some degree of probability and represent an optimal solution for a number
variables and factors
Working in agile environment and coordinate with other roles and team members
Work in multi-disciplinary team, ability to communicate with the domain and subject matter experts
Embrace online learning, continuously improve your knowledge, use professional networks andcommunities
Story Telling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding new
knowledge and progress to completion
Ethics and responsible use of data and insight delivered, awareness of dependability (data scientist isa feedback loop in data driven companies)

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Practical Application of the CF-DS• Basis for the definition of the Data Science Body of Knowledge(DS-BoK) and Data Science Model Curriculum (MC-DS)– CF-DS => Learning Outcomes (MC-DS) => Knowledge Areas (DS-BoK)– CF-DS => Data Science taxonomy of scientific subjects and vocabulary• Data Science professional profiles definition– Extend existing EU standards and occupations taxonomies: e-CFv3.0,
ESCO, others• Professional competence benchmarking– For customizable training and career development– Including CV or organisational profiles matching• Professional certification– In combination with DS-BoK professional competences benchmarking• Vacancy construction tool for job advertisement (for HR)– Using controlled vocabulary and Data Science Taxonomy

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DSP Profiles mapping to ESCO Taxonomy
High Level Groups• DSP Profiles mapping to corresponding CF-DS Competence Groups– Relevance level from 5 – maximum to 1 – minimum
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Data Science Professions Family

Icons used: Credit to [ref] https://www.datacamp.com/community/tutorials/data-science-industry-infographic

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Competences Map to Knowledge and Skills (2)• Competence is a demonstrated ability to apply knowledge,
skills and attitudes for achieving observable resultsCompetences

Knowledge

Ability toperform Org
functions

SkillsExperience &Workplace Train

Education (BoK)
Professional
ProfilesEDISON Warsaw June 2017

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EDSF for Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Data Science Model Curriculum (MC-DS)• Set Learning Units mapped to CF-DS Learning and DS-BoK Knowledge
Areas/Units– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training for self-made Data Scientists practitioners– Education and training marketplace: Courses catalog and repository
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Data Science Body of Knowledge (DS-BoK)
DS-BoK Knowledge Area Groups (KAG)• KAG1-DSA: Data Analytics group including
Machine Learning, statistical methods,
and Business Analytics• KAG2-DSE: Data Science Engineering group
including Software and infrastructure engineering• KAG3-DSDM: Data Management group including data curation,
preservation and data infrastructure• KAG4-DSRM: Research Methods group• KAG5-DSBP: Business process management group• Data Science domain knowledge to be defined by related expert groups

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Example DS-BoK Knowledge Areas definition andmapping to existing BoKs and CCS (2012)•

Mapping suggested to CCS2012
and existing BoKsEDISON Warsaw June 2017

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Data Science Model Curriculum (MC-DS)
Data Science Model Curriculum includes• Learning Outcomes (LO) definition based on CF-DS– LOs are defined for CF-DS competence groups and for all
enumerated competences• LOs mapping to Learning Units (LU)
– LUs are based on CCS(2012) and universities best practices– Data Science university programmes and courses inventory(interactive)
http://edison-project.eu/university-programs-list• LU/course relevance: Mandatory Tier 1, Tier 2, Elective,
Prerequisite• Learning methods and learning models (in progress)

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Example MC-DS Mapping Learning Units to DSBoK and CCS (2012)•

Mapping suggested to ACM CCS2012, DS-BoK and other related BoKsEDISON Warsaw June 2017

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Outcome Based Educations and Training Model

Data Science Competence
Framework (CF-DS)

DS Professional Profiles(DSP-P)

Learning Outcomes (LO)

LearningOutcomes
Outcomes (LO)
Learning(LO)(OBELearning
LearningModel)
Model)(OBE(OBE Learning Model)

Competences
Competences
And
KnowledgeAnd
Units (KU)
Skills- (DS-BoK)
SkillsCompetences
Competences
And (LU)
Learning And
Units
Skills- Skills(MC-DS)

From Competences and DSP
Profilesto Learning Outcomes (LO)andto Knowledge Unites (KU) and
Learning Units (LU)• EDSF allow for customized
educational courses and training
modules design

Tracks/Specialisation
LU/KU/DS-PP
Tracks/Specialisations(basedon DSP-P)
Tracks/Specialisation
LU/KU/DS-PP(LO, LU, KU, courses/modules)

DS Academic Programmes

DS Professional Training

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
Courses
Courses LO/KU/LU/DSP-P

CoursesLO/LU/KU/DS-PP
LO/LU/KU/DS-PP
CoursesModules LO/KU/LU/DSP-P

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Individual Competences Benchmarking
M AT C H I N G – C O M P E T E N C E P R O F I L E S
DSP04 - Data Scientist
DSBPM06
DSBPM05
DSBPM04

DSDA0110
9
8

DSDA02
DSDA03
DSDA04

7

DSBPM03

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA05

6
DSBPM02

DSDA06

5
4

DSBPM01

DSDM01

3
2

DSRM06• Red polygon indicates the chosen
professional profile: Data Scientist(general)• Green polygon indicates the candidate orpractitioner competences/skills profile• Insufficient competences (gaps) arehighlighted in red– DSDA01 – DSDA06 Data Science Analytics– DSRM01 – DSRM05 Data Science Research
Methods

DSDM02

1

0
DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

DSRM01•Can be use for team skills match marking andorganisational skills management[ref] For DSP Profiles definition and for enumerated
competences refer to EDSF documents CF-DS and DSP
Profiles.

DSENG01

DSENG06
DSENG05

DSENG02
DSENG03
DSENG04

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Building a Data Science Team
Data Science
Applications Developer

Data Steward
Data Engineer,
Database Developer

Data Steward

Data Facilities
Operator

Data Scientist
Data Analysis

Researcher(Scientific domain)

Data
SourceDataSource
Data(Experiment,
Driven(Experiment, Data
Application)
DrivenApplication)

Data Entry/
SupportData Science GroupManager,
Data Science Architect

Data Science
Applications Developer

Data Scientist

Data Steward
Data Science Researcher
Business Analyst
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Data Analyst/
Business Analyst

Data Scientist

Data Steward
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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE
Data Science or Data Management Group/Department>> Reporting to CDO/CTO/CEO• Providing cross-organizational(Managing) Data Science Architect (1)services
Data Scientist (1), Data Analyst (1)Data Science Application programmer (2)Data Infrastructure/facilities administrator/operator: storage, cloud,computing (1)• Data stewards, curators, archivists (3-5)

•
•
•
•

Estimated: Group of 10-12 data specialists for research institution of 200300 research staff.

Growing role and demand for Data Stewards and data stewardshipEDISON Warsaw June 2017

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Data Stewards – A rising new role in Data Science
ecosystem• Data Stewards as a key bridging role between Data
Scientists as (hard)core data experts and scientific domain
researchers (HLEG EOSC report)• Current definition of Data Steward (part of Data Science
Professional profiles)– Data Steward is a data handling and management professional
whose responsibilities include planning, implementing and managing(research) data input, storage, search, and presentation.
– Data Steward creates data model for domain specific data, support
and advice domain scientists/ researchers during the whole research
cycle and data management lifecycle.

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Further developments and Next steps (1)••Next EDSF release 2 (planned for June 2017) will link competences to skills andknowledge
Final EDSF project deliverables (due August 2017) will include:– Data Science Education Sustainability Roadmap• Will involve wide consultation with experts community and also with EU policy makers• Will be reviewed by the EDISON Liaisons Groups (ELG)– Certification Framework for at least two levels of Data Science competences
proficiency• Consultation with few certification providers is in the progress•

Toward EDSF and Data Science profession standardisation– ESCO (European Skills, Competences and Occupations) taxonomy – extending with
the Data Science related occupations, competences and skills– CEN TC428 (European std body) – Extending current eCFv3.0 and ICT profiles
towards e-CF4 with Data Science related competences– Work with the IEEE and ACM curriculum workshop to define Data Science Curriculumand extend current CCS2012 (Classification Computer Science 2012)•

Number of Case studies is planned in cooperation with active EU projects EDSA,EOSCpilot, BDVe, etc. (not limited to the project lifetime)

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Further developments and Next steps (2)•The EDISON project legacy will include(linked to the current project website and migrated to CP in the future)– EDSF – EDISON Data Science Framework– Data Science Community Portal (CP) - http://datasciencepro.eu/– EDISON project network including• EDISON Liaison Groups• Data Science Champions conference• Cooperative networks with European Research Infrastructures (e.g. HEP, Bioinformatics,
Environment and Biodiversity, Maritime, etc),• International cooperative links BHEF, APEC, IEEE, ACM• Applications and tools development– Prototypes will be produced in the timeline of the project but further development is asubject to additional funding• Sustainability of the project legacy/products will be ensured by the project
partners voluntarily for the period at least 3 years– EDSF will be maintained by UvA– CP by Engineering (Italy)
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Further developments and Next steps (3)• Further dissemination, engagement and outreach activity– Publishing final deliverables as BCP and books– Data Science Manifesto – Primarily focused on professional and ethical issues in Data
Science, new type of professional– Inter-universities initiative “Data Science for UN’s Sustainable Development Goals” tofocus in-curricula research (projects) on UN priority goals• Wider engagement into EOSC activities related to RI data related skills
management and capacity building

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Summary: Services and References••

EDISON Website http://edison-project.eu/
EDISON Data Science Framework (EDSF)
http://edison-project.eu/edison/edison-data-science-framework-edsf•

Directory of University programs
http://edison-project.eu/university-programs-list•

Community Portal http://datasciencepro.eu/•

Survey Data Science Competences: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession•••

Competences benchmarking and tailored training for practitioners
Data Science Curriculum advice and design for universities
Data Science team building and organizational roles profilingLinks to EDISON Resources• EDISON project website http://edison-project.eu/•

EDISON Data Science Framework Release 1 (EDSF)
http://edison-project.eu/edison-data-science-framework-edsf
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Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles-definition-dsp

Survey Data Science Competences: Invitation to participate
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession

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Other related links•

Amsterdam School of Data Science––•

Research Data Alliance interest Group on Education and Training on Handling of
Research Data (IG-ETHRD)–•

https://ec.europa.eu/digital-single-market/en/news/final-results-european-data-market-studymeasuring-size-and-trends-eu-data-economy

PwC and BHEF report “Investing in America’s data science and analytics talent:
The case for action” (April 2017)–•https://www.rd-alliance.org/groups/education-and-training-handling-research-data.html

Final Report on European Data Market Study by IDC (Feb 2017)–•https://www.schoolofdatascience.amsterdam/
https://www.schoolofdatascience.amsterdam/education/

http://www.bhef.com/publications/investing-americas-data-science-and-analytics-talentBurning Glass Technology, IBM, and BHEF report “The Quant Crunch: How thedemand for Data Science Skills is disrupting the job Market” (April 2017)––

http://www.bhef.com/publications/quant-crunch-how-demand-data-science-skills-disrupting-jobmarket
https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576USEN.PDF

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Additional materials

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OECD and UN on Digital Economy and Data Literacy
OECD• Demand for new type of “dynamic self-re-skilling workforce”• Continuous learning and professional development to become ashared responsibility of workers and organisations[ref] SKILLS FOR A DIGITAL WORLD, OECD, 25-May-2016
http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/ICCP/IIS(2015)10/FINAL&docLanguage=EnUN• Data Revolution Report "A WORLD THAT COUNTS"
Presented to Secretary-General (2014)
http://www.undatarevolution.org/report/• Data Literacy is defined as key for digital revolution• Data literacy = critically analyse data collected anddata visualised
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EDSF Recognition, Endorsement and Implementation• DARE (Data Analytics Rising Employment) project by APEC (Asia
Pacific Economic Cooperation)– DARE project Advisory Council meeting 4-5 May 2017, Singapore• PcW and BHEF Report “Investing in America’s data science andanalytics talent” April 2017
– Quotes EDSF and Amsterdam School of Data Science• Dutch Ministry of Education recommended EDSF as a basis foruniversity curricula on Data Science– Workshop “Be Prepared for Big Data in the Cloud: Dutch Initiatives for
personalized medicine and health research & toward a national action
programme for data science training”, Amsterdam 28 June 2016
– Currently working with Dutch Gov on re-skilling IT/data workers for DSA
competences• European Champion Universities network– 1st Conference (13-14 July, UK), 2nd Conference (14-15 March, Madrid,
Spain)– 3rd Conference 19-20 June 2017, Warsaw
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What challenges related to skills management the
EDSF can help to address?
1. Guide researchers in using right methods and tools, latest Data
Analytics technologies to extracting value from scientific data
2. Educate and train RI engineers dev to build modern data intensive
research infrastructure and understand trends and project for future
3. Develop new data analytics tools and ensure continuous improvement(agile model, DevOps)
4. Correctly organise and manage data, make them accessible (adhering
FAIR principles), education new profession of Data Stewards
5. Help managers to facilitate career dev for researchers and organise
effective teams
6. Ensure skills and expertise sustain in organisation
7. Help research institutions to sustain in competition with industry andbusiness in data science talent hunting

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