Profiling Data Science Education and Training
based onEDISON Data Science Framework (EDSF)
Yuri Demchenko
University of Amsterdam

EDISON – Education for Data Intensive
Science to Open New science frontiers
Grant 675419 (INFRASUPP-4-2015: CSA)AACSB Conference on
Data Science at Business Schools
Amsterdam, May 16 / 17, 2017

Project: Building Data Science Teams that work
Data Science
Applications Developer

Data Steward
Data Engineer,
Database Developer

Data Steward

Data Facilities
Operator

Data Scientist
Data Analysis

Researcher(Scientific domain)

DataSource
DataSource
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
Amsterdam, 17 May 2017

Data Analyst/
Business Analyst

Data Scientist
Data Steward
EDISON Data Science Framework

2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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EDSF: How CF-DS was constructed• Background: Standards and Best Practices• Jobs market analysis: Demanded Data ScienceCompetences and Skills

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EDISON Data Science Framework4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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EDISON Data Science Framework5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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Data Science Professions Family

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

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EDISON Data Science Framework

8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

Business Process
Operations/Stages
••
•
•
•

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

Scientific Methods••••••
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EDISON Data Science Framework

Design Experiment
Collect Data
Analyse Data
Identify PatternsHypothesise Explanation
Test Hypothesis
9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 datamanagement)
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–

Critical thinking, personal, inter-personal communication, team work, professional network

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EDISON Data Science Framework

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

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

0Use appropriatestatistical 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 insightsDSENG01
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 methods

2DSDM01
Develop andimplement datastrategy, 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

3DSDA03
Develop 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

5DSDA05
Use 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

EDISON Data Science Framework

11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

DSDA01109876543210

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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DSP Profiles mapping to ESCO Taxonomy
High Level Groups and CF-DS Competences•DSP Profiles mapping to corresponding CF-DS Competence Groups– Relevance level from 5 – maximum to 1 – minimum

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Education and Training – Part of EDSF
• 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 Framework14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
LearningModel)(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) andLearning 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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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 groupincluding 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 Framework

16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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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– Followed by Ministerial meeting on 14-15 May 2017 in Hanoi, Vietnam•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 for university
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 programmefor 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•

e-IRG workshop on Sustainability on 8-9 June 2017, Malta

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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 yrs– 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

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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/•••

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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