Data Science among other Data Driven Technology
Domains Revisited
Data Science In the Digital Data Driven Economy Ecosystem• Data Science integrates multiple components from different scientificand technology domains to drive data intensive research and emerging
digital technologies development.
• Data Science has emerged as a scientific discipline to become afoundation for academic research and curricula development
Data Science is a complex discipline that uses conceptual andmathematical abstractions and models, statistical methods, together
with modern computational tools to obtain knowledge/derive insightfrom data to uncover correlations and causations in business dataand support decision making in scientific research and businessactivity.

Data Scientist definition by the EDISON Data Science
Framework (EDSF) – Ref. ESDF, 2016, based on NIST SP 1500
A Data Scientist is a practitioner who has sufficient competences andknowledge in the overlapping regimes of expertise in data analytics
skills, domain knowledge, business needs, and programming andsystems engineering expertise to manage the end-to-end scientificmethod process through each stage in the big data lifecycle, till thedelivery of an expected scientific and business value to organizationor project.

Data Science and Data Handling Professions Family –Compliant with ESCO

• Data Science uses Scientific/Research Methods for discovery of a value
from data: new relations, new factors, knowledge for decision support

Data Science Competence Domains

EDISON Data Science Framework (EDSF)
CF-DS

DS-BoK

MC-DS

Competences

Learning Outcomes

Knowledge

Learning Units

SkillsAttitude

DS Prof
ProfilesDirectory Edu&Train
Materials

EDISON Data Science Framework (EDSF) provides
conceptual basis for the Data Science profession
Data Science Core Competences and Skills
Core Data Science competences• Data Science Analytics (including Statistical Analysis, Machine
Learning, Business Analytics)• Data Science Engineering (including Software and Applications
Engineering, Data Warehousing, Big Data Infrastructure and Tools)• Domain Knowledge and Expertise (Subject/Scientific domain related)• Data Management, Data Governance, Stewardship, Curation,Preservation• Research Methods and/vs Business Processes/Operations
Data Science professional skills: Thinking and acting like Data Scientist –required to successfully develop as a Data Scientist and work in Data
Science teams• 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

For more information refer to EDSF Release 3 documents
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
Related publications
The Data Science Framework, A View from the EDISON Project, Editors Juan J. CuadradoGallego, Yuri Demchenko, Springer Nature Switzerland AG 2020, ISBN 978-3-030-51022-0,
ISBN 978-3-030-51023-7 (eBook, printed book)Yuri Demchenko, et al, Data Scientist Professional Revisited: Competences Definition andAssessment, Professional Development and Education Path Design, International
Conference on Big Data and Education (ICBDE2021), February 3-5, 2021, London, UK

EDSF is maintained by the EDISON Community Initiative
https://github.com/EDISONcommunity/EDSF
Grant number 675419 (2015-2017)(INFRASUPP-4-2015: CSA)

Contact: Yuri Demchenko <y.demchenko@uva.nl>