Data literacy can be improved by teaching data science methods and tools. Both method-kit and tool-kit are eclectic and interdisciplinary, posing potential threats for students’ understanding and motivation, especially at a business school where the focus is not on IT. We found that a broad spectrum of methods and tools can be helpful, rather than a hindrance, when teaching data science to absolute beginners. This case study investigates how teaching and learning workflows can be improved when systematically deploying a wide array of digital tools in an introductory online data science class at a large German business school, taught in the winter term of 2020, during the COVID-19 pandemic lockdown. The class was offered to all students of the school as part of the extracurricular program. Several such tools were selected, introduced, and used, with a special emphasis on tools to enhance teaching and learning productivity in and between live sessions. Tools included: the GitHub global code repository, the Emacs editor with ESS and Org-Mode markdown, Kahoot! Games, screencasting, video tutorials and podcasts, external assignment tools like DataCamp and Celonis, and a Moodle platform to pull all of these together. Participation and explicit student feedback were very positive and have already initiated curricular changes at the school. In this paper, the course rationale, goals, content, tools, and preliminary findings are presented and discussed, and an outlook for further research is given. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Teaching Data Science in a Synchronous Online Introductory Course at a Business School – A Case Study