Implementing Learning Paths into Data Science Courses - a Qualitative Approach

Driven by technological advancements in generative AI and the shortage of data professionals in the European labour market, a growing interest in data science education has led to the development of numerous data science curricula. However, a standardized competency framework for data scientists has not yet been established. Moreover, data scientists have shifted from a generalist approach to focusing on specialised roles within the data ecosystem. As a result, data science curricula have become more specialised, often including a comprehensive introductory phase followed by in-depth studies in specific areas. However, many students struggle to combine the diverse competencies and knowledge elements a data science degree teaches. To address this challenge, this research project focuses on developing a data science framework that identifies interdependencies between competencies and knowledge elements, enabling students to choose personalized learning paths based on their individual goals and prior knowledge. This paper introduces a competency network to create personalized learning paths for an introductory data science course. It will be based on professionally logical interdependencies, which will be evaluated and optimised through the analysis of expert interviews. The goal is to positively impact students' self-efficacy, motivation, and learning outcomes by providing a structured and adaptable learning experience.