Data Skills for Everyone! (?)–An Approach to Assessing the Integration of Data Literacy and Data Science Competencies in Higher Education

• Published in: Journal of statistics and data science education Vol. 33; no. 1; pp. 90 - 115
• Main Authors: Coners, André, Matthies, Benjamin, Vollenberg, Carolin, Koch, Julian
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis Ltd 2025; Taylor & Francis Group
• Subjects: Business Administration; College Science; Comparative Analysis; Comparative Education; Competence; Computer Science; Content Analysis; Curricula; Curriculum; Data literacy; Data science; Database Management Systems; Educational Practices; Educational Research; Educational Technology; Higher education; Individualized Instruction; Influence of Technology; Information literacy; Information Science; Information Systems; Information Technology; Portfolio Assessment; Research Universities; Science Curriculum; Science Education; Science Instruction; Scientific Literacy; Statistical Data
• DOI: 10.1080/26939169.2024.2334408
• ISSN: 2693-9169

The proficient handling of data can undoubtedly be regarded as a key skill for the future. However, the need for data competencies is not limited to traditional professions in the information technology environment but is rather necessary across industries and work fields. Consequently, there is a call to integrate such Data Literacy and Data Science competencies into higher education teaching across the breadth of study programs. This study, descriptive in nature, sheds light for the first time on the status quo of this integration. For this purpose, a “Data Science Dictionary” has been developed, that structurally maps a corresponding curriculum of the German Informatics Society (GI). Using quantitative content analysis, more than 13,950 distinct modules from three German study programs (Business Administration, Business & Information Systems Engineering, Computer Science) at two different types of universities are examined. As a result of this comparative study, concise “teaching portfolios” are compared between disciplines, whereby the prevalence, proportion, and depth of the competencies integrated into the study programs become transparent. Thus, this approach can provide a basis for discourse on the future integration of data competencies into, for example, business study curricula; furthermore, it can track the resulting progress in a longitudinal study. Supplementary materials for this article are available online.