Sustainable development of the modern data-driven economy requires re-thinking and re-design of both traditional educational models and existing courses reflecting the multi-disciplinary nature of Data Science and its application domains. However, at present time most of the existing university curricula and training programs are built based on available courses. Moreover, they cover a limited set of competences and knowledge areas that are related to multiple Data Science and general data management professional profiles and organizational roles, required both in research and industry.

In conditions of continuous technology development and shortened technology change cycle, Data Science education requires an effective combination of theoretical, practical, and workplace skills. The importance of effective use of existing data analytics and data management platforms and tools is growing. For this reason, the hands-on experience and their implementations need to be generically incorporated into modern curriculum design.

This Research Topic is intended to allow educators, researchers, and practitioners to present their research works and share experience on a wide spectrum of research and best practices on education for Data Science, Data Management and Machine Learning, and other emerging data-driven and data-powered professional domains.

This collection focuses on defining essential Data Science and Big Data competences, skills, and (body of) knowledge that could be effectively used for the development of instructional methodologies, teaching techniques, competences assessment, and organizational capacity building and skills management.

It will also benefit from wider sharing of standardized education frameworks for Data Science (such as the EDISON Data Science Framework resulted from EU funded project EDISON) that provide a common approach for Data Science definition and tailored curriculum design for various target professional groups.

The Research Topic welcomes papers discussing topics related to teaching methods, platforms, and best practices in the following areas applied to Data Science and Big Data or closely related to them:

- Data Science competences and skills, Body of Knowledge and Model Curriculum
- Data Management and Data Stewardship, FAIR Data Principles
- Data Stewardship Competences, Education and Training
- Visualization and storytelling in Big Data and Data Analytics
- Data Science process automation: DataOps, MLOps
- Data Science and Big Data education and professional training
- Experiences, Case Studies and Lessons Learned
- Novel or updated teaching methods, online education adoption
- Data Science education as a foundation for Artificial Intelligence
- Establishing Data Science as a new scientific and academic discipline

Keywords: Data Science, Curriculum Design, EDISON Data Science Framework, Data Science Competence Framework, Data Science Body of Knowledge, Data Science Model Curriculum, Data Management, Data Stewardship, DataOps