EDISON Data Science Framework (EDSF)
as a foundation
for the Data Science profession definition andsustainable skills development
Yuri Demchenko, EDISON Project
University of Amsterdam

Workshop "Wissenschaft im Digitalen
Wandel"6 June 2017, Mannheim, Germany
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Outline•

Background–•

EDISON Data Science Framework (EDSF)––•

Research Data Management
Professional issues in Data Science

Activities and initiatives worldwide to establish Data (Science) professions family–•

From Data Science Competences and Skills to Body of Knowledge and Model Curriculum
Data Science Profession Profiles family and organisational skills management

Use of EDSF for Data Science curricula design––•

Recent EU Initiatives, European Digital Single Market (DSM) and demand for data enabled skills

BHEF, DARE/APEC, IEEE/ACM

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)

Mannheim, 6 June 2017https://rd-alliance.org/dataharvest-report-sharing-dataknowledge-jobs-and-growth.html

Emergence of Cognitive Technologies(IBM Watson and others)
EDISON Data Science Framework (EDSF)

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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

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

Address growing demand and shortage of data-related skills

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

Addresses the need for digital and complementary skills, ensure young talents flow into
data driven research and industry
Launch Digital Skills and Jobs Coalition (1st December 2016, Brussels) to develop
comprehensive national digital skills strategies by mid-2017

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Demand for Data Science and data related
professions•

McKinsey Global Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp– Estimated gap of 140,000 - 190,000 data analytics
skills by 2018•

UK Big Data skills report 2014
– 6400 UK organisations with 100+ staff will have implemented
Big Data Analytics by 2020
– Increase of Big Data jobs from 21,400 (2013) to 56,000 (2017)•

IDC Report on European Data Market (2015)– Number of data workers 6.1 mln (2014) - increase 5.7% from 2013
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply 509,000 (2014) or 7.5%•

HLEG report on European Open Science Cloud (October 2016) identified
need for data experts and data stewards– Recommendation: Allocate 5% grant funding for Data management and preservation– Estimation: More than 80,000 data stewards (1 per every 20 scientists)– Core Data Experts (as defined) need to be trained and their career perspective
improved

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Industry report on Data Science Analytics and Dataenabled skills demand•

IDC Report on European Data Market (2015)– Number of data workers 6.1 mln (2014) - increase 5.7% from 2013
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply 509,000 (2014) or 7.5%•

PwC and BHEF report “Investing in America’s data science and analytics
talent: The case for action” (April 2017)– http://www.bhef.com/publications/investing-americas-data-science-and-analytics-talent•Burning Glass Technology, IBM, and BHEF report “The Quant Crunch:
How the demand for Data Science Skills is disrupting the job Market”(April 2017)– http://www.bhef.com/publications/quant-crunch-how-demand-data-science-skillsdisrupting-job-market– 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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EDISON Services for Data Science and 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 )Mannheim, 6 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

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•

Other definitions to admit such features as
–
–
–

•
•

[ref] Legacy: NIST BDWG
definition of Data Science

Ability to solve variety of business problems
Optimize performance and suggest new services for the organisation
Develop a special mindset and be statistically minded, understandraw data and “appreciate data as a first class product”

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 “21st Century Skills”– Inter-personal skills and team work, cooperativeness•

Important aspect of integrating Data Scientist (team) into organisation structure––

General Data Science (and Data) literacy for all involved roles and management
Role of Data Scientist: Provide a kind of literacy advice and guidance to organisation

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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
Hypothesise Explanation
Test Hypothesis

Business Operations
•
•
•
•
•

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

12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-design13

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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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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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, initiative,
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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21st Century Skills – Different views of the same••

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New model in skills management: Shared
responsibility between employee and employer
Millennials factor: development and mobility

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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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Mapping DS Profiles to Competence Map
Collect
Data

Analyse
Data

DSP
20

DSP
09
DSP DOMAIN
10

DSP
21

EXPERTISE

DesignExperimentDSP
11

Data
Management
DSP
13
DSP
22

RESEARCH
DSP
MODELS08

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ANALYTICSYSTEMSDSP
17
DSP Data Science
15 ENGINEERING
DSP
DSP
16
18

Test
Hypothesis

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

Science

DSP ANALYTICSDATA05 DSP
06

DSP
PROCESSES
14
ALGORITHMS

•
•
•
•
•

DSP
04
Data

SCIENCEDSP
12

Data Science Competences
include 5 groups

DSP
07
DSP
19

DSP
Hypothesise
03
Explanation

Business Process
Operations/Stages
Identify
Patterns
DSP
01

ScientificMethods
DSP
02

Business Process
Management(for biz competences)

EDISON Data Science Framework (EDSF)

•
•
•
•
•

DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design

Scientific Methods
•
•
•
•
•
•

Design Experiment
Collect Data
Analyse Data
Identify Patterns
Hypothesise Explanation
Test Hypothesis
21

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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Competences Map to Knowledge and Skills• 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
ProfilesMannheim, 6 June 2017

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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: Scientific/Research Methods group
KAG5-DSBP: Business process management group•

Data Science domain knowledge to be defined by related expert groups••

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

Mapping suggested to CCS2012
and existing BoKs

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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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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
EDISON Data Science Framework (EDSF)

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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• 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 andadvice domain scientists/ researchers during the whole research
cycle and data management lifecycle.

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New courses currently missing• Data Management / Research Data Management– Data Curation, Data Stewardship• Professional issues in Data Science– Including Ethics and responsible use of Data Science

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KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas(1) Data Governance(2) Data Architecture(3) Data Modelling and Design(4) Data Storage and Operations(5) Data Security(6) Data Integration andInteroperability(7) Documents and Content(8) Reference and Master Data
(9) Data Warehousing and BusinessIntelligence(10) Metadata(11) Data Quality

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Other Knowledge Areas motivated by
RDA, European Open Data initiatives,
European Open Data Cloud(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data, Open
Access, ORCID(15) Responsible data use•

Highlighted in red: Considered (Research)
Data Management literacy (minimum
required knowledge)

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34

Research Data Management Model Curriculum –Part of the EDISON Data Literacy Training
A. Use cases for data management and stewardship•
Preserving the Scientific Record
B. Data Management elements (organisational and individual)•
Goals and motivation for managing your data•
Data formats•
Creating documentation and metadata, metadata for discovery•
Using data portals and metadata registries•
Tracking Data Usage•
Handling sensitive data•
Backing up your data•
Data Management Plan (DMP) - to be a part of hands on session

Collaboration with the Research
Data Alliance (RDA) on developing
model curriculum on Research
Data Literacy:• Modular, Customisable,
Localised, Open Access• Supported by the network of
trainers via resource swap
board

C. Responsible Data Use Section (Citation, Copyright, Data Restrictions)
D. Open Science and Open Data (Definition, Standards, Open Data use and reuse, open government data)•
Research data and open access•
Repository and self- archiving services•
ORCID identifier for data•
Stakeholders and roles: engineer, librarian, researcher•
Open Data services: ORCID.org, Altmetric Doughnut, Zenodo
E. Hands on:a) Data Management Plan designb) Metadata and toolsc) Selection of licenses for open data and contents (e.g. Creative Common and Open Database)Mannheim, 6 June 2017

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Professional Issues in Data Science• Data Science subjects/disciplines/components technologies• Research Data Management and RDM Plan
– Including data format, metadata• Open Data and Open Science• Data related skills and career management– Including Data Science certification• Responsible Data Science and professional ethics• 21st Century Skills• Data protection, data privacy, data security

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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•

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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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
–
–

–
–

•

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://www.rd-alliance.org/groups/education-and-training-handling-research-data.html

PwC and BHEF report “Investing in America’s data science and analytics talent:
The case for action” (April 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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European Open Science Cloud (EOSC)

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HLEG report on European Open Science Cloud(October 2016) – Demand for Data Scientists/Stewards
Realising the European Open Science Cloud. First report andrecommendations of the Commission High Level Expert Group on theEuropean Open Science Cloud, October 2016
https://ec.europa.eu/research/openscience/pdf/realising_the_european_open_science_cloud_2
016.pdf•

Definition of the Data Steward as a distinctive role and profession–•

Estimation: More than 80,000 data stewards to serve 1.7 mln scientists in Europe(1 per every 20 scientists)–•

Core Data Experts need to be trained and their career perspective improved

Based on 5% grant funding for Data management and preservation

Clash of cultures between domain specialists and e-Infrastructure specialists(i.e. IT/Computer Science)

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HLEG report on European Open Science Cloud(October 2016) – Demand for Core Data Expertise
Realising the European Open Science Cloud. First report andrecommendations of the Commission High Level Expert Group on theEuropean Open Science Cloud, October 2016
• Recommendation: Allocate 5% grant funding for Data management andpreservation• Estimation: More than 80,000 data stewards to serve 1.7 mln scientists in
Europe (1 per every 20 scientists)• Core data experts need to be trained and their career perspective
improved

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HLEG EOSC Report Essentials – Core Data Experts•

Core Data Experts is a new class of colleagues with core scientific professional
competencies and the communication skills to fill the gap between the two
cultures.
– Core data experts are neither computer savvy research scientists nor are they hardcore data or computer scientists or software engineers.
– They should be technical data experts, though proficient enough in the content domain
where they work routinely from the very beginning (experimental design, proposal
writing) until the very end of the data discovery cycle– Converge two communities:• Scientists need to be educated to the point where they hire, support and respect Core Data
Experts• Data Scientists (Core Data Experts) need to bring the value to scientific research andorganisations•

Implementation of the EOSC needs to include instruments to help train, retain
and recognise this expertise,– In order to support the 1.7 million scientists and over 70 million people working ininnovation.

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EOSC Report Recommendations –Implementation on training and skills•

I2.1: Set initial guiding principles to kick-start the initiative as quickly as
possible. -> Bridge two cultures/communities––•

A first cohort of core data experts should be trained to translate the needs for data driven science
into technical specifications to be discussed with hard-core data scientists and engineers.
This new class of core data experts will also help translate back to the hard- core scientists thetechnical opportunities and limitations

I3: Fund a concerted effort to develop core data expertise in Europe.
––

Substantial training initiative in Europe to locate, create, maintain and sustain the required core dataexpertise.
By 2022, to train (hundreds of thousands of) certified core data experts with a demonstrable
effect on ESFRI/e-INFRA activities and prospects for long-term sustainability of this criticalhuman resource••

Consolidate and further develop assisting material and tools for Data Management Plans and Data
Stewardship plans (including long-term preservation in FAIR status)

I7: Provide a clear operational timeline to deal with the early preparatory
phase of the EOSC.
–

Define training needs for the necessary data expertise and draw models for the necessary
training infrastructure

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Approach Skills demand for EOSC
• Task is not for one community or one project– Need collaboration between different stakeholders andcommunities: academia, research, industry, public sector• Task is not for science or RI only in isolation fromindustry and academia• Needs strong conceptual approach– Use science to solve the problems of science• Standardisation is an important factor of sustainability
and development
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Data Scientist and Subject Domain Specialist•

Subject domain components
–
–
–
–
–

•

Model (and data types)
Methods
Processes
Domain specific data and presentation/visualization methods
Organisational roles and relations

Data Scientist is an assistant to Subject Domain Specialists– Translate subject domain Model, Methods, Processes into abstract data driven form
– Implement computational models in software, build required infrastructure and tools– Do (computational) analytic work and present it in a form understandable to subject
domain– Discover new relations originated from data analysis and advice subject domain
specialist– Present/visualise information in domain related actionable way– Interact and cooperate with different organizational roles to obtain data and deliver
results and/or actionable data

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Data Science and Subject Domains
Data Science domain components
Data structures &databases/storage
Visualisation• Abstract data driven
math&compute models• Data Analytics
methods• Data and Applications
Lifecycle Management

Crossorganisational
assistive role

Data Scientist functions is to translate between two domains

Domain specific components
Domain specificdata & presentation(visualization)• Models (and data types)• Methods• Processes

Organisationalroles

Data Scientist role is to maintain the Data Value Chain (domain specific):•
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Data Integration => Organisation/Process/Business Optimisation => Innovation
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EDSF: How CF-DS was constructed• Background: Standards and Best Practices• Jobs market analysis: Demanded Data Science
Competences and Skills

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Background: Standards and Best Practices•

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

CWA 16458 (2012): European ICT Professional Profiles Family Tree
– 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 andIEEE Computer Science Curricula 2013 (CS2013)

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Background: Standards and Best Practices•

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

CWA 16458 (2012): European ICT Professional Profiles Family Tree
– 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 andIEEE Computer Science Curricula 2013 (CS2013)

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Jobs market analysis: Demanded Data Science
Competences and Skills• Initial Analysis (period Aug – Sept 2015) -> Continuous
monitoring (in development)– IEEE Data Science Jobs (World but majority US)• Collected > 120, selected for analysis > 30

– LinkedIn Data Science Jobs (NL)
• Collected > 140, selected for analysis > 30

– Existing studies and reports + numerous blogs & forums• Analysis methods– Data analytics methods: classification, clustering, feature extraction– Research methods: Data collection - Hypothesis – Artefact Evaluation– Expert evaluation by EDISON Liaison Groups (ELG), multiple
workshops

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Skills and Capacity Management Challenges
Addressed by EDSF
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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