Teaching Data Science to Non-Computer Science Students: A Learner-Centered Approach

The aim of our paper is to provide good practices for teaching data science to non-computer science students and find out how we can motivate female students to take MINT (Mathematics, Computer science, Natural science and Technology) classes. The analyzed data science course is offered at University of Vienna in the Business Analytics, Data Science, and Digital Humanities faculties as part of the masters' programs. In this work, we outline the course structure and present findings gained through teaching it. Moreover, we highlight the main differences between the editions of the course. Anonymous surveys, grade analysis, and student interviews, show that female students achieved the same learning outcomes as their male peers in all the terms as measured by the total score and all three sub-scores, namely the project submission, the mid-term quiz, and the final quiz. In 2020 and 2021, no significant differences could be found in student performance by their attended study program. In 2022, a difference could be found in the midterm exam; however, no significant difference could be found in the final exam and the project work, indicating that the course was able to harmonize the learners' diverse background. This suggests that the diverse learning opportunities offered in this course fit the individual needs of students of different backgrounds, which increases the accessibility of the data science field among non-computer science students. We propose the course as described in this paper as a good practice of teaching 21st century digital skills in a studentcentered way and conclude the paper with five recommendations.