Data Science Profession and Education
EDISON Data Science Framework (EDSF)
as a foundation to establish Data Science profession

Yuri Demchenko, EDISON Project
University of Amsterdam
EUROCON 2017
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

6 July 2017, Ohrid, Macedonia

Outline•••

Background: Reports, Studies, Initiatives
Who is Data Scientist
EDISON Data Science Framework (EDSF)– Defined Competences and skills – walk through– Data Science Professional Profiles Family••

Data Science Body of Knowledge and Model Curriculum
Use of EDSF
– Competence Benchmarking– Data Science Teams– Tailored Training Programs•

Discussion and Questions

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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)

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

Emergence of Cognitive Technologies(IBM Watson, Cortana 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
Scientist validates and designs additional texts or experiments

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Data Driven Victories and Failures - Politics
Very high impact events and facts•

US Election 2012 – Obama’s campaign and rise of Big Data analytics– Micro-targeting and Social Networks analysis•

Brexit 2016
– “Data driven Brexit” – first serious ring for right use of Data Science technologies•

US Election 2016
– Clinton’s campaign – “Data driven” but using only upper layer of Social Network (SN)web
– Trump’s campaign – Targeting bottom SN web and “forgotten people not to beforgotten”• Matt Oczkowski, leader on Trump’s campaign: “If he was going to win this election, it was going
to be because of a Brexit style mentality and a different demographic trend than other people
were seeing.”•

France election 2017
– Awakening

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Data-Driven Brexit: A Wakeup Call for Analysts
By Barry Devlin, June 28, 2016
Book: In Data We Trust:
How Customer Data is
Revolutionising Our
Economy (Aug 2012)• A strategy for
tomorrow's data world

Data-Driven Brexit: A
Wakeup Call for Analysts
By Barry Devlin, June 28,
2016

•
•

Article “In Data we trust” by T.Edsall in The
New York Times
Multimillion-dollar contract for data
management and collection services
awarded May 1, 2013 to Liberty Work (for
Republicans) to build advanced list ofvotersEUROCON 2017

• There are significant lessons for believers in data-driven business tolearn from how data was and wasn’t used for decision making before,
during, and after the Brexit vote.
• Human attitude -- including emotion, intuition, and social empathy -and motivation are at the heart of decision making and the action that
follows• Information will only be accepted when it conforms to preconceived
notions. Expertise is not sufficient and, in extremis, will be dismissedwith ridicule.
Data Science Profession and Education
6

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-talent2.35 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
Commonly required work experience 3-5 yrs
Influenced by EDISONEUROCON 2017

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PwC&BHEF: Demand for DSA enabled jobs
Demand for business people withanalytics skills, not just data
scientists• Of 2.35 million job postings in
the US– 23% Data Scientist– 67% DSA enabled jobs•

Strong demand for managers
and decision makers with Data
Science (data analytics)
skills/understanding– Challenge to deliver actionableknowledge and competences to
CEO level managers

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PwC&BHEF: Skills that are tough to find
To be mapped to Competences,
Knowledge, Skills and Personal (soft) Skills

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PwC&BHEF: Data Science and Analytics skills, by
2021: The supply-demand challenge

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Challenge for Education:
Sustainable ICT and Data Skills Development• 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 the whole skills management paradigm– Dynamic (self-) re-skilling: Continuous professional development andshared 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 )EUROCON 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

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Data Scientist definitions: From Math to Hacking•

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Data Science Profession and Education

Strongly depend on the
background of the Data
Scientist

14

Becoming a Data Scientist by Swami Chandrasekaran(2013) http://nirvacana.com/thoughts/becoming-a-data-scientist/• Good andpractical advice
how to learn
Data Science,
step by step
• Follow the route

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Data Scientist definition
Based on the definitions by NIST SP1500 – 2015, extended by EDISON•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 deliveryof an expected scientific and business value toorganisation or project.

•Core Data Science competences and skills groups
–
–
–

•

EDISON identified 2 additional competence groups demanded by organisations––•

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)
Data Management, Data Governance, Stewardship, Curation, Preservation
Research Methods and/vs Business Processes/Operations

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-design17

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-design18

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

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

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
relationsDSBPM01
Understand business and provide
insight, translate unstructured
business problems into an abstract
mathematical framework

DSDA02
Use statistical techniq
to deliver insights

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

DSRM01Create new understandings andcapabilities by using scientific/
research methods or similar domain
related development methods2

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

DSRM02
Direct systematic study toward afuller knowledge or understandingof the observable facts

DSBPM02
Participate strategically andtactically in financial decisions

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

DSENG03
Develops specialized tools

DSRM03
Undertakes creative work

DSBPM03
Provides support services to other

4

DSDA04
Analyze complex data

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

5

DSDA05Use different analytics

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

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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

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Key Data Science Analytics Competencesby EDISON and DARE Project for APEC countries•

Data Science Analytics, Data Science Engineering, Data Management,
Research Methods, Domain Knowledge– 3 levels of proficiency defined: Entry/Associate, Professional, Expert/Lead•

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•

21st Century Skills and Data Science Professional skills

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

8.

9.
10.
11.
12.
13.
14.
15.

Recognise value of data, work with raw data, exercise good data intuition, use SN and open data
Accept (be ready for) iterative development, know when to stop, comfortable with failure, accept thesymmetry of outcome (both positive and negative results are valuable)
Good sense of metrics, understand importance of the results validation, never stop looking at
individual examples
Ask the right questions
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
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
Recognise what things are important and what things are not important (in data modeling)
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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Data Science Professional Skills:
Thinking and Acting like Data Scientist
1.
2.
3.
4.
5.
6.
7.
8.

9.
10.
11.
12.
13.
14.
15.

Recognise value of data, work with raw data, exercise good data intuition , use SN andopen data
Accept/be ready for iterative development, know when to stop, comfortable with failure,
accept the symmetry of outcome (both positive and negative results are valuable)
Good sense of metrics, understand importance of the results validation, never stop
looking at individual examples
Ask the right questions
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
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
Recognise what things are important and what things are not important (in data modeling)
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 and communities
Story Telling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding new knowledge and progress tocompletion
Ethics and responsible use of data and insight delivered, awareness of dependability (data scientist is a feedback loop in data
driven companies)

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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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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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Data Science Professions Family

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

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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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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 and Project Management group
KAG5-DSBA: Business Analytics and Business Intelligence
KAG* - DSDK: 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 BoKs

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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 BoKs

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Data Science Profession and Education31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)(OBE
Learning
Model)(OBE Learning Model)(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
DSBPM03
DSBPM02
DSBPM01
DSRM06

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA01
10
9
8
7
6
5
4
3
2
1
0

DSDA02
DSDA03
DSDA04
DSDA05
DSDA06
DSDM01– DSDA01 – DSDA06 Data Science Analytics– DSRM01 – DSRM05 Data Science Research
Methods

DSDM02

DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

DSRM01• 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•

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 stewardship
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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•

EDSF Release 2 (mid July 2017) will link competences to skills and knowledge–••

Data Science Body of Knowledge and Model Curriculum ready for direct use

Development of EDSF based tools: Self assessment and Market
monitoring
Certification Framework for at least two levels of Data Science
competences proficiency: Associates and Professionals– Data Science knowledge and competences for decision makers•

Toward EDSF and Data Science profession standardisation– ESCO (European Skills, Competences and Occupations) taxonomy– 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)•

Data Science Manifesto – Primarily focused on Professional and Ethicalissues in Data Science, new type of professional

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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
–
–

–
–

•

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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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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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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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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