Edison Data Science Framework: Part 1. Data Science Competence Framework (Cf-Ds) Release 2
Creators: name: Demchenko, Yuri
name: Belloum, Adam
name: Wiktorski, Tomasz
Publisher: name: Zenodo
Year: 2017
Subjects: Data Science Competence Framework (CF-DS)
Data Science Body of Knowledge (DS-BoK)
Data Science Model Curriculum (MC-DS)
Data Science Professional Profiles (DSPP)
Data Science
EDISON Data Science Framework

description: The EDISON project is designed to create a foundation for establishing a new profession of Data Scientist for European research and industry. The EDISON vision for building the Data Science profession will be enabled through the creation of a comprehensive framework for Data Science education and training that includes such components as Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK) and Data Science Model Curriculum (MC-DS), Data Science Professional Profiles (DSPP). This will provide a formal basis for Data Science Profession definition and the professional certification, organizational and individual skills management and career transferability.<br>
The definition of the Data Science Competence Framework (CF-DS) is a cornerstone component of the whole EDISON framework. CF-DS will provide a basis for Data Science Body of Knowledge (DS-BoK) and Model Curriculum (MC-DC) definitions, and further for the Data Science Professional profiles (DSPP). The CF-DS is defined in compliance with the European e-Competence Framework (e-CF3.0) and provides suggestions for e-CF3.0 extension with the Data Science related competences and skills.<br>
The intended EDSION framework comprising of the mentioned above components will provide a guidance and a basis for universities to define their Data Science curricula and courses selection, on one hand, and for companies to better define a set of required competences and skills for their specific industry domain in their search for Data Science talents, on the other hand. Similar to e-CF3.0, the proposed CF-DS will provide a basis for building interactive/web based tool for individual or organizational Data Science competences benchmarking and building the customized Data Science education and training program.<br>
This document presents ongoing results of the Data Science Competence Framework definition based on the analysis of existing frameworks for Data Science and ICT competences and skills, and supported by the analysis of the demand side for Data Scientist profession in industry and research.<br>
The presented CF-DS defines five groups of competences for Data Science that include the commonly recognised groups Data Analytics, Data Science Engineering, Domain Knowledge (as defined in the NIST definition of the Data Scientist) and other two groups Data Management and Scientific Methods and Project Management that are recognised to be important for the successful work of Data Scientist.<br>
The identified competences are provided as enumerated list of competence groups. It is also complemented with the related skills and knowledge topics.  Release 2 CF-DS is extended with the Data Science professional skills ("Thinking and acting as a Data Scientist") and 21st Century skills that are important for successful work of Data Scientist in a multi-subject teams.

The proposed CF-DS is compatible with the industry adopted e-CFv3.0 defintion and provides suggestiond for e-CFv3.0 extension with the Data Science and Analytics related competences.