Coordination and cooperation to establish new
profession of Data Scientist for European
Research and Industry
Yuri Demchenko, EDISON
University of Amsterdam

The Data Science Conference 2016
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

21-22 April 2016, Chicago

Outline•

Background and motivation– Demand for Data Science and data related professions– European initiatives related to Digital Single Market (DSM) and demand to data relatedcompetences and skills•

EDISON Data Science Framework– From Data Science Competences to Body of Knowledge and Model Curriculum•••

Data Science Competence Framework: Essential competences and skills
Taxonomy: Data Science occupations family
Data Science Body of Knowledge (DS-BoK)••

Knowledge areas and academic disciplines

Further steps

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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 (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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Recent European Commission Initiatives
Digitising 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 is exploding, including such as (Data
Scientist) combining data analytics and business or engineering skills.
–•

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

The forthcoming New Skills Agenda for Europe (exp May 2016) to address the need for
digital and complementary skills, ensure young talents flow into data driven research andindustry

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

Raise awareness and change incentive structures for academics, industry and public
services to share their data, and improve data management training, literacy and data
stewardship skills
Address growing demand and shortage of data-related skills and lack of recognition of theirvalue (in all sectors).

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EDISON Framework: Building the Data Science
Profession
CF-DS

•

DS-BoK

MC-DS

EOEE & ETMplz

Taxonomies & Vocabulary

Edu&Train Inventory

Foundation & Concepts

Services

Sustainability:• Roadmap• Community
Portal• Certification• Professional
community/
association

Biz Model

EDISON Framework components••••

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
Data Science Taxonomies and Scientific Disciplines Classification–•

Linked to e-CFv3.0, ACM CCS (2012) and ESCOEOEE - EDISON Online Education Environment

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Background Frameworks and Standards• 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
– Intended inclusion of the Data Science occupations family – end of 2016

• ACM Classification of Computer Science – CCS (2012)– ACM Computer Science Body of Knowledge (CS-BoK) and ACM and IEEE Computer
Science Curricula 2013 (CS2013)
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EDISON Data Science Competence Framework• How it was made• 5 main Data Science competences groups• Skills, tools and languages• Suggested extensions and identified profiles• Data Science occupations family

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Demanded Data Science Competences andSkills: Jobs market analysis• Initial Analysis (period Aug – Sept 2015)– 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– Using manually data analytics methods: classification, clustering, expert
evaluation– Research methods: Data collection - Hypothesis – Artefact - Evaluation• Observations– Many job ads don’t use Data Scientist as a definite profession:• Data Science competences/skills are specified as part of traditional ICT
professions/positions– Many academic openings without specified skills profile– Explicit Data Scientist jobs specify wide variety of expectedfunctions/responsibilities and required skills and knowledge
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Identified Data Science Competence Groups•

Traditional/known Data Science competences/skills groups include
–
–
–

•

Data Analytics or Business Analytics or Machine Learning
Engineering or Programming
Subject/Scientific Domain Knowledge

EDISON identified 2 additional competence groups demandedby organisations[ref] Legacy: NIST BDWG
definition of Data Science– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

Other skills commonly recognized aka “soft skills” or “social intelligence”–•

All groups need to be represented in Data Science curriculum and training programmes–•Inter-personal skills or team work, cooperativeness

Challenging task for Data Science education and training

Another aspect of integrating Data Scientist into organisation structure–––

General Data Science (or Big Data) literacy for all involved roles and management
Common agreed and understandable way of communication and information/data presentationRole of Data Scientist: Provide such literacy advice and guiding to organisation

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Data Science Competence Groups - Research
Data Science Competence
includes 5 areas/groups
•
•
•
•
•

Data Analytics
Data Science Engineering
Domain 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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Building Data Science Profession

Operations Strategy
Plan
Design & Deploy
Monitor & Control
Improve & Re-design10Data Science Competences Groups – Business
Data Science Competence
includes 5 areas/groups

DOMAIN
EXPERTISE

•
•
•
•
•

DesignOptimisation

RESEARCH

DATA

DATA
ANALYTICS

Scientific Methods

SCIENCEANALYTIC ALGORITHMS
SYSTEMS

Monitoring

Modelling
ENGINEERINGCOMPETENCES

Business Process
Management

Data
Management
Execution
The Data Science Conference 21-22 April 2016

Data Analytics
Data Science Engineering
Domain Expertise
Data Management
Scientific Methods (or Business
Process Management)

ScientificMethods
Building Data Science Profession

•
•
•
•
•
•

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

Business Process
Operations/Stages
•
•
•
•
•

DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design11Identified Data Science Competence Groups
Data Analytics (DA)

Data Management/
Curation (DM)
Use appropriate statistical Develop andtechniques on available implement data
data to deliver insights
strategy

DS Engineering (DSE)

Scientific/Research Methods (DSRM)

DS Domain Knowledge(including Business Apps)

Use engineering principles toresearch, design, or develop
structures, instruments,
machines, experiments,
processes, systems, theories,
or technologies

Create new understandings andcapabilities by using the scientific
Understand business andmethod's hypothesis, test, and evaluation provide insight, translatetechniques; critical review; or similar
unstructured businessengineering research and development
problems into an abstract
methods
mathematical framework
Use predictive analytics
Use data to improve existing
Directsystematic
study
towardafuller2 to analyse big data andservices or develop new
knowledge or understanding of the
discover new relations
fundamental aspects of phenomena and services
Develops specialized data
Develop data models analysis tools to supportof observable facts, and discovers new
including metadata
executive decision making
approaches to achieve goals
Participate strategically andResearch and analyze
Integrate different data
Undertake creative work, making
3 complex data sets,tactically in financial
source and provide forsystematic use of investigation ordecisions that impact
combine different sources further analysis
experimentation, to discover or revise
Design, build, operatemanagement andand types of data toknowledge of reality, and uses thisrelational non-relationalorganizations
improve analysis.
databases
knowledge to devise new applications
Develop and apply
Recommends business
4 Develop specialized
related strategic
Develop and maintain computational solutions toanalytics to enablea historical data
domain related problems
objectives andagile decision
repository of analysis using wide range of data
Apply ingenuity to complex problems,
alternatives andmakingdevelop innovative ideas
analytics platforms
implements themCollect and manage
Ability to translate strategies into action
5
Provides scientific, technical,
different source of dataplans and follow through to completion.
Develop solutions for secureand analytic support services
and reliable data accessto other organisational roles
Analyse multiple data sources
Develop algorithms to analyse Influence the development of
6
Visualise complex and multiple source of data
for marketing purposesorganizational objectives
variable data.
Prototype new data analytics
Analyse customer data to
7applications
identify/optimise customer
The Data Science Conference relations actions

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21-22 April 2016

Building Data Science Profession

12

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
Math & Stats apps & 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
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 Social Intelligence–

Personal, inter-personal communication, team work (also called social intelligence or soft skills)

The Data Science Conference 21-22 April 2016

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Data Science Skill Groups related to Competences
Data Analytics andMachine Learning

Data Management/
Curation

Data Science
Engineering (hardware
and software)

Scientific/ Research
Methods

1

Artificial intelligence,machine learning

Design efficientalgorithms for accessing
and analyzing large
amounts of data

Interest in data science2

Machine Learning andStatistical Modelling

Manipulating andanalyzing complex, highvolume, highdimensionality data fromvarying sources
for data improvement

Big Data solutions andadvanced data mining
tools

Machine learning
solutions and patternrecognition
techniques

Data models anddatatypes

Supervised andunsupervised learning

3

4
5

6
7
8

9

Personal/Interpersonalcommunication, teamwork

Application/subject
domain (research or
business)

Communication skills

Recommender or
Ranking system

Analytical, independent,
critical, curious and focusedon results

Inter-personal intrateam and external
communication

Data Analytics forcommercial purposes

Multi-core/distributed
software, preferably in a
Linux environment

Confident with large data
sets and ability to identify
appropriate tools andalgorithms

Network of contacts in
Big Data community

Data sources andtechniques for businessinsight and customer
focus

Handling vast amountsof data

Databases, databasesystems, SQL and NoSQL

Data mining

Experience of working
with large data sets

Statistical analysis
languages and tooling

Flexible analytic approach toachieve results at varying
levels of precision
Exceptional analytical skills

Markov Models,Conditional Random
Fields
Logistic Regression,
Support Vector
Machines
Predictive analysis
and statistics(including Kaggle
platform)(non)relational and (un)structured data

Cloud powered
applications design(Artificial) Neural
Networks

Metadata annotationand management

Statistics

Data citation, metadata,

Mechanism Design
and/or Latent Dirichlet
AllocationGame Theory

Copyright and IPR

Cloud based data
storage and data
management
Data management
planning

10
PID (*)The Data Science Conference 21-22 April 2016

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Suggested e-CF Competences for Data Science:
Next eCF Workshop meeting – 14 April 2016
A. PLAN and Design (9 basic competences)•
A.10* Organisational workflow/processes model definition/formalisation•
A.11* Data models and data structures
B. BUILD: Develop and Deploy/Implement (6 basic competences)•
B.7* Apply data analytics methods (to organizational processes/data)•
B.8* Data analytics application development•
B.9* Data management applications and tools
15 Data Science Competences proposed•
B.10* Data Science infrastructure deployment
C. RUN: Operate (4 basic competences)•
C.5* User/Usage data/statistics analysis•
C.6* Service delivery/quality data monitoringcovering different organizational roles andworkflow stages• Data Scientist roles are crossing
multiple org roles and workflow stages

D. ENABLE: Use/Utilise (12 basic competences)•
D10. Information and Knowledge Management (powered by DS)•
D.13* Data presentation/visualisation, actionable data extraction•
D.14* Support business processes/roles with data and insight (support to D.5, D.6, D.7, D.12)•
D.15* Data management/preservation/curation with data and insight
E. MANAGE (9 basic competences)•
E.10* Support Management and Business Improvement with data and insight (support to E.5, E.6)•
E.11* Data analytics for (business) Risk Analysis/Management (support to E.3)•
E.12* ICT and Information security monitoring and analysis (support to E.8)

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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

The Data Science Conference 21-22 April 2016

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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)

The Data Science Conference 21-22 April 2016

• Models (and data types)• Methods• Processes

Building Data Science Profession

Organisational
roles

17

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
Scientistfunctionsto translate
between two
Valleyof Death
forisDS
Competences& domains
Skills

Domain specific components
Domain specificdata & presentation(visualization)

The Data Science Conference 21-22 April 2016

• Models (and data types)• Methods• Processes

Building Data Science Profession

Organisational
roles

18

Possible Data Scientist profiles/roles
as extension to CWA16458 (2012)• Data Analyst, Business Analyst– Data Mining– Machine Learning• Digital Librarian, Data Archivist, Data Curator, Data Steward– Data Management related competences• Data Science Engineer/Administrator/Programmer– Data analytics applications development– Scientific programming– Data Science/Big Data Infrastructure development/operation• Data Science Researcher– Data Science research methods– Data models and structures• Data Scientist in subject/research domain• Research e-Infrastructure brings its own specifics to requiredcompetences and skills definition
The Data Science Conference 21-22 April 2016

Building Data Science Profession

19Data Science occupations in ESCO taxonomy (1)Professionals
Science andengineering
professionals

Data Science
Professionals

Data Science
professionals notelsewhere classified

Data Scientist

Data Science Researcher(Big) Data Analyst
Data Science (Application)
Programmer
Business Analyst
Database and network
professionals

Information andcommunications
technology
professionals

Data Science
technology
professionals

Large scale (cloud) data
storage designers andadministrators

Large scale (cloud) database
designer*)

Database designers andadministrators

Large scale (cloud) databaseadministrator*)

Database and network
professionals notelsewhere classified

Scientific databaseadministrator*)

Data handlingprofessionals notelsewhere classified

Digital Librarian

Data Archivist
Data Steward
Data curator
The Data Science Conference 21-22 April 2016

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Data Science occupations in ESCO taxonomy (2)Technicians and associate professionals
Science andengineering associate
professionals

Data Science
Technology
Professionals

Data Infrastructureengineers andtechnicians

Big Data facilities Operators

Large scale (cloud) data
storage operators
Database and network
professionals notelsewhere classified

Scientific database operator*)

Managers
Production andspecialised services
managers

Data Science/Big Data
Infrastructure Managers

Data Science/Big Data
Infrastructure Manager
Research InfrastructureManagers

RI Manager
RI Data storage facilities
manager

Clerical support workers
General andkeyboard clerks
Data handling
support workers(alternative)

Data and information
entry and access

Digital Archivists andLibrarians

Digital Librarian

Data Archivist
Data Steward
The Data Science Conference 21-22 April 2016

Building Data Science Profession

Data curator

21

Data Science or Data Management Group/Department:
Organisational structure and staffing
•
•
•
•
•

Manager
Data Science Architect
Data Analyst
Data Science Application programmer
Data Infrastructure/facilities administrator/operator: storage,
cloud, computation• Data stewards

The Data Science Conference 21-22 April 2016

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Data Science or Data Management Group/Department:
Organisational structure and staffing - EXAMPLE••••Managing Data Science Architect (1)Data Analyst (1)Data Science Application programmer (2)Data Infrastructure/facilities administrator/operator: storage,
cloud, computing (1)• Data stewards (3-5)
Group of 10-12 specialists for research institution of 200-300
staff.

The Data Science Conference 21-22 April 2016

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Education and Training• Foundation and methodological base
– Data Science Body of Knowledge (DS-BoK)• Taxonomy and classification of Data Science related scientific subjects– Instructional methodologies and teaching models• Platforms and environment– Virtual labs, datasets, developments platforms– Access control and accounts/identity management– Online education environment and courses management• Services– Individual benchmarking and profiling tools (competence assessment)– Knowledge evaluation tools– Certifications and training– Education and training marketplace: Courses catalog and repository
The Data Science Conference 21-22 April 2016

Building Data Science Profession

24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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Building Data Science Profession25

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

The Data Science Conference 21-22 April 2016

Other Knowledge Areas motivatedby European Open Data
initiatives, European Open Data
Cloud, and RDA (Research Data
Alliance)(12) PID, metadata, data registries(13) Data Management Plan
(14) Open Science, Open Data,
Open Access, ORCID(15) Responsible data use

Building Data Science Profession

26

Topics considered for the Data Management(Literacy) Training – Working draft
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
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)
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Further Steps•

Define a taxonomy and classification for DS competences and skills as a basis for moreformal CF-DS definition–•

Closer look at skills, tools and platforms

Run surveys for target communities
https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession– Plan a number of key interviews, primarily experts and top executives at universities and companies•

Proceed with suggested e-CF3.0 extensions and participate in the next e-CF meetings
–

•

•
•

Provide feedback and contribution to ESCO with the definition of the Data Science
profession family
Suggest ACM CCS2012 Classification extensions and officially contact ACM
Involve academic and industry experts and professional organisations to the definition of
DS-BoK following from CF-DS–•

Talk to national e-CF bodies or adopters if available

Link/Map to taxonomy of academic and educational and training courses

Create open community forum to collect contribution–

CF-DS and DS-BoK documents are on public comments available from EDISON website
http://www.edison-project.eu/data-science-competence-framework-cf-ds
http://www.edison-project.eu/data-science-body-knowledge-ds-bok

The Data Science Conference 21-22 April 2016

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Discussion• Questions• Observations• Survey: Invitation to participate

https://www.surveymonkey.com/r/EDISON_project_-_Defining_Data_science_profession
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EXAMPLE: e-CF Dimensions for RI Technical(based on RDA IG-ETRD work)•

Dimension 1: 5 e-Competence areas, derived from the ICT processes present in
RI development, management and operation:
A. PLAN and DESIGN
B. DEVELOP and DEPLOY/IMPLEMENT (BUILD)
C. OPERATE (RUN)
D. USE: UTILISE (ENABLE)
E. MANAGE•

Dimension 2: A set of reference competences for each area; currently identified
35 competences that are mapped from the general eCFv3.0.

•

Dimension 3: Proficiency levels of each e-Competence, currently using e-CF
approach that provides European reference level specifications on e-Competencelevels e-1 to e-5, which are related to the EQF levels 3 to 8.
– Increasing number of practicing data scientists have master degree and higher•

Dimension 4: Samples of knowledge and skills related to e-Competences indimension 2. They will be provided to add value and context and are not intended
to be exhaustive.
– Knowledge identification is important to build Body of Knowledge

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EXAMPLE: Use of e-CF3.0 for Defining Profile of
RI Technical (part of RDA IG-ETRD work)
A. PLAN and DESIGN

D. USE: UTILISE (ENABLE)

A.2. Service Level Management
A.3. Product / Service Planning
A.5. Application Design
A.4. Architecture Design

D.1. Scientific Applications Integration (on running RI)
D.5. Data collection and preservation
D.4. New requirements and change Identification
D.6. Education and Training Provision

Additional

A.6. Sustainable Development
A.7. Innovating and Technology Trend Monitoring
A.8. Business/Research Plan Development and Grant
application
A.1. RI and Research Strategy Alignment
B. BUILD: DEVELOP and DEPLOY/IMPLEMENT

B.1. Application Development (Reqs Engineering,
Function Specs, API, HCI)
B.2. Component Integration
B.3. Testing (RI services and Scientific Apps)
B.4. Solution/Apps Deployment
Additional

B.5. Documentation Production
B.6. Systems Engineering (DevOps)
C. OPERATE (RUN)

C.1. User Support
C.2. Service Delivery
C.3. Problem Management

Additional

D.2. Information Security Strategy Development
D.3. RI/ICT Quality Strategy Development
D.7. Purchasing/Procurement
D.8. Contract Management
D.9. Personnel Development
D.10. Dissemination and outreach
E. MANAGE

E.1. Overall RI management (by systems andcomponents)
E.5. Information/Data Security Management
Additional

E.6. Data Management (including planning and lifecycle
management, curation)
E.4. RI Security and Risk/Dependability Management
E.2. Project and Portfolio Management
E.3. ICT Quality Management and ComplianceE.7. RI/IS Governance

Additional

C.4. Change Support (Upgrade/Migration)

The Data Science Conference 21-22 April 2016

Building Data Science Profession

31Definitions (according to e-CFv3.0)• Competence is a demonstrated ability to apply knowledge, skills andattitudes for achieving observable results.
– Competence vs Competency (e-CF vs ACM)
• Competence is ability acquired by training or education (linked to learning outcome)• Competency is similar to skills or experience (acquired feature of a person)• Competence is not to be confused with process or technology concepts
such as, ‘Cloud Computing’ or ‘Big Data’. These descriptions represent
evolving technologies and in the context of the e-CF, they may be
integrated as elements within knowledge and skill examples.
• Knowledge in the context of competence definition is treated as
something to know, to be aware of, familiar with, and obtained as a partof education.
• Skills is treated as provable ability to do something and relies on the
person’s experience.

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EDISON Approach: CF-DS and e-CFv3.0•

Competence Framework for Data Science (CF-DS) definition will be built based on
European e-Competence framework for IT (e-CFv3.0)– Linking scientific research lifecycle, organizational roles, competences, skills and knowledge– Defining Data Science Body of Knowledge (DS-BoK)– Mapping CF-DS and DS-BoK to academic disciplines in a DS Model Curriculum (MC-DS)• Multiple use of e-CFv3.0 within ICT
organisations• Provides basis for individual career
path, competence assessment, training
and certification• EDISON CF-DS will be used for
defining DS-BoK and MC-DS, linking
organizational functions and required
knowledge• Provide basis for individual (self)
training and certification

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e-CFv3.0 Internal Structure: Refactoring for CF-DS• s• 4 Dimensions– Competence Areas– Competences– Proficiency levels– Skills and Knowledge• 5 Competence Area defined by ICT
Business Process stages– Plan– Build– Run– Enable– Manage-> Refactor to Scientific Research
cycle/workflow (and linked to Scientific Data
Lifecycle)– See example of RI manager at IG-ETRD wiki
and meeting• Each competence has 5 proficiency level– Ranging from technical to engineering tomanagement to strategist/expert level• Knowledge and skills property are definedfor/by each competence and proficiency
level (not unique)
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CWA 16458 (2012): European ICT Professional
Profiles Family Tree•

European ICT Profile Family Tree – Generation 1 and 2 as a shared European reference

The Data Science Conference 21-22 April 2016

Building Data Science Profession

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