Applied Data Analysis: A Problem-based Learning Approach

This paper examines the transition of a conventional multivariate statistics module to a problem-based learning module, first implemented in 2021. The primary objective was to enhance students’ problem-solving skills, bridging the gap between mathematical concepts and real-world applications. The approach was implemented to instil a deeper understanding of real-world data analysis, emphasising the interpretation of domain specific problems in mathematical terms and the production of reports for industrial stakeholders.

Findings indicate that the integration of problem-based methods not only improved students’ comprehension of statistical techniques but also fostered a more profound appreciation for their practical utility in diverse professional contexts. The problem-solving cycle, a central component of the approach, guided students in critically analysing complex challenges and formulating data driven solutions. Furthermore, this study emphasises the potential for replicating the industrial study group experience within an undergraduate teaching environment.

Adopting a problem-based learning approach in the teaching of data analysis empowers students to apply their analytical skills effectively to real-world scenarios, strengthening their capacity to communicate insights and solutions to industrial stakeholders. The study underscores the value of aligning educational practices with the demands of data-driven industries, providing students with a competitive advantage in future research and the job market. The study is descriptive and reflective in nature.