EDISON Data Science Framework:
Foundation for Building Data Science Profession
for Research and Industry
Yuri Demchenko, EDISON Project
University of Amsterdam
Big Data Innovation Conference BDIC2016

16-17 November 2016, Frankfurt, Germany
EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)

Outline•

Background and motivation– Demand for Data Science and data related professions– European initiatives related to Digital Single Market and Digital Skills Agenda•

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

Data Science Competence Framework: Essential competences and skills
Data Science Professional profiles and taxonomy– Mapping to competences and team profiling•

Educational and training focus– Data Science Body of Knowledge (DS-BoK)– Data Science Model Curriculum (MC-DS)– Example Research Data Management Literacy curriculum•

Further developments to formalise EDSF and Data Science profession
establishment– Invitation to cooperation

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EDISON Data Science Framework

Slide_2

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 gainfrom the 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

https://www.rd-alliance.org/

The Data Harvest:
How sharing researchdata can yieldknowledge, jobs andgrowth.

NIST Big Data Working Group (NBD-WG)
http://bigdatawg.nist.gov/

An RDA Europe Report.
December 2014

ISO/IEC JTC1 Big Data Study Group (SGBD)
http://jtc1bigdatasg.nist.gov/home.php

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

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EDISON Data Science Framework3The Fourth Paradigm of Scientific Research
1. Theory and logical reasoning
2. Observation or Experiment––E.g. Newton observed apples falling to design his theory ofmechanics
But Gallileo Galilei made experiments with falling objects from thePisa leaning tower

3. Simulation of theory or model–

Digital simulation can prove theory or model

4. Data-driven Scientific Discovery (aka Data Science)– More data beats hypnotized theory

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EDISON Data Science Framework4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 Big Data 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
be forgotten”– 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.”

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EDISON Data Science Framework5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 tobuild advanced list of voters

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• 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.
EDISON Data Science Framework
6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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EDISON Data Science Framework7

Recent European Commission Initiatives 2016
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 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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EDISON Data Science Framework8OECD on Digital Economy• Demand for new type of “dynamic self-re-skilling workforce”• Continuous learning and professional development tobecome a shared 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=En

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EDISON Data Science Framework (EDSF)
Release 1 (October 2016)
DS-BoK

MC-DS

EDISON Online
Educational Environt

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
•
•
•
•
•
•

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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EDISON Data Science Framework

10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 expected scientific and business value
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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EDISON Data Science Framework11

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
– Expected inclusion of the Data Science occupations family – mid 2017

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

How it was made5 main Data Science competences groups
Skills, tools and languages
Practical use of the CF-DS
Data Science Professional profiles and occupations family

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EDISON Data Science Framework

13Demanded Data Science Competences andSkills: Jobs market analysis and stakeholders• 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, expert evaluation– Research methods: Data collection - Hypothesis – Artefact Evaluation

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Identified Data Science Competence Groups•

Commonly accepted 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– Data Management, Curation, Preservation– Scientific or Research Methods and/vs Business Processes/Operations•

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

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

Challenging task for Data Science education and training

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

General Data Science (and Data) literacy for all involved roles and management
Common agreed and understandable way of communication and information/data presentationRole of Data Scientist: Provide a kind of literacy advice and guidance 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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EDISON Data Science Framework

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

16

Data Science Competences Groups – Business
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 Process
Operations/Stages
•
•
•
•
•
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EDISON Data Science Framework

DesignModel/Plan
Deploy & Execute
Monitor & Control
Optimise & Re-design17

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
relations2

DSDA02
Use statistical techniq
to deliver insights

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

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

3

DSDA03Develop specialized …

DSDM03
Collect integrate data

4

DSDA04
Analyze complex data

5

DSDA05Use different analytics

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

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

DSBPM02
Participate strategically andtactically in financial decisions

DSENG03
Develops specialized tools

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

DSDM04Maintain repository

DSENG04
Design, build, operate

DSRM04
Translate strategies into actions

DSBPM04
Analyse data for marketing

DSDM05Visualise cmplx data

DSENG05
Secure and reliable data

DSRM05Contribute to organizational goals

DSBPM05
Analyse optimise customer relatio

EDISON Data Science Framework

DSBPM03
Provides support services to other18

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 Social Intelligence–

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

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

DSDA0110
9
8

DSDA02
DSDA03
DSDA04

7

DSBPM03

Individual Education/Training Pathbased on Competence benchmarking

Candidate - Data Scientist

DSDA05

6
DSBPM02

DSDA06

5
4

DSBPM01

DSDM01

3
2

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

DSDM02

1

0
DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

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

DSENG01

DSENG06
DSENG05

DSENG02
DSENG03
DSENG04

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EDISON Data Science Framework

21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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Data Science Competence Framework (CFDS) and DSP Profiles Mapping

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EDISON Data Science Framework

24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

25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
Group Manager
services
Data Science Architect
Data Analyst
Data Science Application programmer
Data Infrastructure/facilities administrator/operator: storage, cloud,computation• Data stewards

•
•
•
•
•

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EDISON Data Science Framework

26

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.

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EDISON Data Science Framework27

[ref] Cortnie Abercrombie, What CEOs want from CDOs and how to deliveron it [online] http://www.slideshare.net/IBMBDA/what-ceos-want-from-cdosand-how-to-deliver-on-it

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EDISON Data Science Framework28

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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EDISON Data Science Framework29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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EDISON Data Science Framework30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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EDISON Data Science Framework33Outcome Based Educations and Training Model

Data Science Competence
Framework (CF-DS)

DS Professional Profiles(DSP-P)

Learning Outcomes (LO)

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

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

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

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

DS Academic Programmes

DS Professional Training

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

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

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EDISON Data Science Framework34

KAG3-DSDM: Data Management group: data curation,
preservation and data infrastructure
DM-BoK version 2 “Guide for
performing data management”– 11 Knowledge Areas

Other Knowledge Areas motivated by
RDA, European Open Data initiatives,
European Open Data Cloud(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

DTW2016 12 Dec 2016(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)

EDISON Data Science Framework

35

Further Developments and Actions•

Run surveys and key interviews for target communities– Primarily experts, visionaries and top executives at universities and companies••

Work with champion universities to practically validate the proposed EDSF
Run a serial of the Champion Universities conferences: Spring 2017 and Summer
2017– Extend scope to Champions, Adopters, Ambassadors and Professionals•••

Involve academic and industry experts and professional organisations to thedefinition of DS-BoK following from CF-DS
Formally provide suggestions to ESCO for the definition of the Data Science
professional profiles (occupations) family
Formally provide suggestions for e-CF3.0 extensions for Data Science to
CEN/PC 428– Involve national e-CF bodies and adopters where available•

Suggest required ACM CCS(2012) Classification extensions and proposal for
Data Science curriculum definition

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36

Discussion• Questions• Comments• Invitation to contribution and cooperation:– Forum, EDISON Liaisons Groups, Champions Conference (Spring & Summer 2017)•

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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EDISON Data Science Framework

37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 Data Science Framework

38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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EDISON Data Science Framework

39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
EDISON Data Science Framework40