TY - GEN
T1 - Teaching Data Literacy: Bridging Business Demands and Curriculum Essentials in Dynamic Learning Environments
AU - Tockner, Reinhard
AU - Zehetner, Andreas
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The labor market evolution highlights data literacy as vital for future graduates, not just as a skill but as a cornerstone for resource efficiency and data-driven decision-making. This study explores instructional methods going beyond traditional coding-centric approaches, to enhance data literacy for non-STEM business students. It supports National Academies’ and supranational organizations’ push for specialized data education across academic and corporate domains. Three key teaching approaches are detailed: coding approaches using Phyton, R, etc., data-centric tools like Microsoft Excel, and visualization-based techniques (Power BI, Tableau), evaluating their strengths, challenges, and applicability across fields. In a pilot case study, an innovative “Data Literacy” module within an MBA program is presented and detailed. This module, embracing technical, methodological, and application-related aspects, intends to equip business students with versatile skills for data-centric environments. The paper concludes by stressing the need for ongoing improvement in data literacy teaching modules to business students in higher education institutions, considering their positive impact in real-world settings. The success of these practical modules among business students signifies the promising integration of data literacy into professional curricula, preparing graduates more effectively for tomorrow’s data-driven workplaces.
AB - The labor market evolution highlights data literacy as vital for future graduates, not just as a skill but as a cornerstone for resource efficiency and data-driven decision-making. This study explores instructional methods going beyond traditional coding-centric approaches, to enhance data literacy for non-STEM business students. It supports National Academies’ and supranational organizations’ push for specialized data education across academic and corporate domains. Three key teaching approaches are detailed: coding approaches using Phyton, R, etc., data-centric tools like Microsoft Excel, and visualization-based techniques (Power BI, Tableau), evaluating their strengths, challenges, and applicability across fields. In a pilot case study, an innovative “Data Literacy” module within an MBA program is presented and detailed. This module, embracing technical, methodological, and application-related aspects, intends to equip business students with versatile skills for data-centric environments. The paper concludes by stressing the need for ongoing improvement in data literacy teaching modules to business students in higher education institutions, considering their positive impact in real-world settings. The success of these practical modules among business students signifies the promising integration of data literacy into professional curricula, preparing graduates more effectively for tomorrow’s data-driven workplaces.
KW - Data literacy
KW - coding approach
KW - data visualization
KW - data-centric approach
KW - teaching data management
UR - https://www.scopus.com/pages/publications/105001336322
U2 - 10.1007/978-3-031-83523-0_41
DO - 10.1007/978-3-031-83523-0_41
M3 - Conference contribution
SN - 978-3-031-83522-3
T3 - Lecture Notes in Networks and Systems
SP - 445
EP - 456
BT - Futureproofing Engineering Education for Global Responsibility - Proceedings of the 27th International Conference on Interactive Collaborative Learning, ICL 2024
A2 - Auer, Michael E.
A2 - Rüütmann, Tiia
PB - Springer
ER -
