working paperlecture
EPSILON - Data Science for Social Good (DSSG) - Lecture Notes
Abstract
The EPSILON project - European Platform for Social Data Science Incubation, Learning, Operation and Network - aims to bridge the gap between data science and social good by fostering impactful, research-driven initiatives. Co-funded by the European Union, EPSILON provides open educational resources and best practices for leveraging data science methodologies in addressing societal challenges. This document synthesizes OER learning materials created by the EPSILON project team, structured across six modules, covering fundamental concepts in data science, ethical considerations, comparative analyses of "Data for Good" initiatives, and real-world case studies. Topics range from big data processing workflows and data science project life cycle management to best practices in data science volunteer management and data science applications in social sectors. The materials highlight the growing real-world impact of volunteer-driven, nonprofit data initiatives, with an emphasis on the importance of ethical responsibility, sustainable project design, and scientific collaboration. EPSILON was co-funded by the European Union (2021-1-DE01-KA220-HED-000029711). All views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or DAAD. Neither the European Union nor the granting authority can be held responsible for them- Arbeitspapier
- working paper
- info:eu-repo/semantics/lecture
- Sozialwissenschaften, Soziologie
- Social sciences, sociology, anthropology
- Data Science; Social Good; Data Science for Social Good; DSSG; Statistics; Data Processing; Ethical Research; Project Lifecycle; Best Practices; Collaborative Networking; Open Educational Resources, OER
- Forschungsarten der Sozialforschung
- Erhebungstechniken und Analysetechniken der Sozialwissenschaften
- Research Design
- Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
- 20500