EDISON Data Science Framework: A Foundation for Building Data Science Profession for Research and Industry
Data Science is an emerging field of science, which requires a multi disciplinary approach and should be built with a strong link to emerging Big Data and data driven technologies, and consequently needs re thinking and re design of both traditional educational models and existing courses. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects by design the whole lifecycle of data handling in modern, data driven research and the digital economy. This paper presents the EDISON Data Science Framework (EDSF) that is intended to create a foundation for the Data Science profession definition. The EDSF includes the following core components: Data Science Competence Framework (CF DS) Data Science Body of Knowledge (DS BoK) Data Science Model Curriculum (MC DS) and Data Science Professional profiles (DSP profiles) The MC DS is built based on CF DS and DS BoK, where Learning Outcomes are defined based on CF DS competences and Learning Units are mapped to Knowledge Units in DS BoK. In its own turn, Learning Units are defined based on the ACM Classification of Computer Science (CCS2012) and reflect typical courses naming used by universities in their current programmes. The paper provides example how the proposed EDSF can be used for designing effective Data Science curricula and reports the experience of implementing EDSF by the Champion Universities that cooperate with the EDISON project.
2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)