EDISON Data Science Framework: A Foundation for Data Science
Competence Management and Curricula Development
EDISON Data Science Framework (EDSF)
EDISON Data Science Framework (EDSF) provides
conceptual basis for the Data Science profession•••••

CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSPP - Data Science Professional Profiles
Data Science Taxonomy and Scientific Disciplines Classification

Service and operational components of the Data
Science professional ecosystem• Community Portal (CP) including• EOEE - EDISON Online Education Environment• Education and Training Marketplace and Directory• Professional certification and career development support

Data Science Competence Framework (CF-DS)

Outcome Based Educations and Training Model:
From Competences and DSP Profiles to Learning Outcomes (LO)and to Knowledge Unites (KU) and Learning Units (LU)

Data Science Competences
includes 5 areas/groups• DSDA - Data Analytics• DSENG - Data Science Engineering• DSDK - Domain
Knowledge/Expertise• DSDM - Data Management• DSRM - Scientific Methods (orBusiness Process Management)

Scientific Methods• Design Experiment• Collect Data
• Analyse Data
• Identify Patterns• Hypothesise Explanation• Test Hypothesis

CF-DS is defined in compliance with the European e-Competence framework for ICT (e-CFv3.0 - 2015)

MATCHING – COMPETENCE PROFILES
DSP04 - Data Scientist

Data Science Body Of Knowledge (DS-BoK)DSBPM06
DSBPM05
DSBPM04
DSBPM03

Data Science Body of Knowledge Area Groups
DS-BoK Knowledge Area Groups (KAG) are defined in compliance with the CF-DS• KAG1-DSDA: Data Analytics group including Machine Learning, statisticalmethods, and Business Analytics• KAG2-DSENG: Data Science Engineering group including Software andinfrastructure engineering• KAG3-DSDM: Data Management group including data curation, Research DataManagement, Open Data, Open Access• KAG4-DSRM: Scientific and Research Methods, use cases/practices• KAG5-DSBPM: Business Process Management (data driven)
Includes selected KAG and KU defined in ACM CCS (2012), PMI-BoK, BABOK,SWEBOK, DM-BoK,

Acknowledgement
EDISON project is supported by the Horizon 2020 Programme
Grant number 675419 (INFRASUPP-4-2015: CSA).

DSBPM02
DSBPM01
DSRM06

Candidate - Data Scientist
DSDA01109876543210

Individual Education/Training Pathbased on Competence benchmarking

DSDA02DSDA03
DSDA04
DSDA05
DSDA06
DSDM01
DSDM02

DSRM05

DSDM03

DSRM04

DSDM04

DSRM03

DSDM05

DSRM02

DSDM06

DSRM01DSENG06
DSENG05

DSENG01

DSENG04

DSENG02
DSENG03

Red polygon indicates the chosen professional profile: Data
Scientist (general)
Green polygon indicates the candidate or practitioner
competences/skills profile
Insufficient competences (gaps) are highlighted in red••

DSDA01 – DSDA06 Data Science Analytics
DSRM01 – DSRM05 Data Science Research Methods

Can be use for team skills match marking and organisationalskills management[ref] For DSP Profiles definition and for enumerated competencesrefer to EDSF documents CF-DS and DSP Profiles.

For more information refer to EDISON documents
EDSF - http://edison-project.eu/edison-data-science-framework-edsf
CF-DS: http://edison-project.eu/data-science-competence-framework-cf-ds
DS-BoK: http://edison-project.eu/data-science-body-knowledge-ds-bok
MC-DS: http://www.edison-project.eu/data-science-model-curriculum-mc-ds
DSP Profiles: http://www.edison-project.eu/data-science-professional-profiles-dsp

Authors: Yuri Demchenko, Adam Belloum, Wouter Los, Cees de Laat
Contact: Yuri Demchenko <y.demchenko@uva.nl>