EDISON Data Science Framework (EDSF)

Data Science Professional Education and Training Model:
Competence driven curriculum design
EDISON Data Science Framework (EDSF) Release 3

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)

Data Science Body Of Knowledge (DS-BoK)

Data Science Competences
includes 5 areas/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-DSRMP: Research Methods and Project Management, use cases/practices• KAG5-DSBPM: Business Process Management (data driven)• DSDA - Data Analytics• DSENG - Data ScienceEngineering• DSDK - Domain
Knowledge/Expertise• DSDM - Data Management• DSRMP – Research Methods
and Project Management

Includes selected KAG and KU defined in IEEE/ACM CCS (2012), PMI-BoK, BABOK,SWEBOK, DM-BoK, ACM CS-BoK, compliance with CMMI DMM, IT-CMF

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

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

CF-DS is defined in compliance with the European e-Competence framework for ICT (e-CFv3.0 – 2015/2017)
Dimension 1 - Competence group
Dimension 2 – Competence definition

Dimension 3 - Proficiency level (EQL compliant)
Dimension 4 – Knowledge (KA & KU) and Skills

MATCHING – COMPETENCE PROFILES
DSP04 - Data Scientist
DSBPM06
DSBPM05
DSBPM04
DSBPM03
DSBPM02
DSBPM01
DSRM06

Candidate - Data Scientist
DSDA01109876543210

DSDA02
DSDA03
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 andorganisational skills management[ref] For DSP Profiles definition and for enumerated
competences refer to EDSF documents CF-DS and DSP
Profiles.

For more information refer to EDISON Release 3 documents

Competence Driven Curriculum Design outcome• Data Science curriculum for different types of learners and professional profiles• Virtual Labs and Project Development Environment that can combine local
university facility and use cloud based Big Data and Data analytics facilities andservices on demand

EDSF github project - https://github.com/EDISONcommunity/EDSF
EDISON Community work area and discussions https://github.com/EDISONcommunity/EDSF/wiki/EDSFhome
Mailing list - edison-net@list.uva.nl

EDSF is maintained by the EDISON Community Initiative
https://github.com/EDISONcommunity/EDSF
Contact: Yuri Demchenko <y.demchenko@uva.nl>
Grant number 675419 (2015-2017)(INFRASUPP-4-2015: CSA)

Individual Education/Training Pathbased on Competence benchmarking