EDISON Data Science Framework: Part 1. Data Science Competence Framework (CF-DS) Release 1

The presented Data Science Competence Framework is a product of the EDISON project 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). This will provide a formal basis for Data Science Profession definition and the professional certification, organizational and individual skills management and career transferability. 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 certification. 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. 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. 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. 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 extend them with the two new groups Data Management and Scientific Methods (or Business Process management for business related occupations) that are recognised to be important for the successful work of Data Scientist but are not explicitly mentioned in existing frameworks. The identified competences are provided as enumerated list of competence groups. It is also complemented with the related skills (including both hard and soft skills) and a list of required Big Data and analytics tools and programming languages. The report suggests possible extensions to e-CF3.0 on the Data Science related competences. It also provides observation that e-CF model, which is primarily based on the organisational workflow, is not natively compatible with the Data Science functions that are rather data life cycle oriented