Treffer: Beyond topical similarities: semantic differences and the challenge of interdisciplinary course integration in business analytics education

Purpose Business analytics education sits at the intersection of business decision-making, statistical modeling and computational techniques. While accreditation bodies encourage analytics integration into curricula, they provide little guidance on course structuring. Many institutions borrow non-business courses, but their fit within business curricula remains unclear. This study examines whether business and non-business graduate analytics curricula differ beyond topical content and explores implications for interdisciplinary course integration. Design/methodology/approach This study applies natural language processing (NLP) and machine learning to analyze graduate-level course descriptions from business and non-business analytics programs. BERTopic identifies latent topical structures, while a neural network-based classifier Neural Network Simultaneous Optimization Algorithm (NNSOA) assesses semantic distinctions. Statistical tests determine whether topics disproportionately represent one domain. Findings Results indicate that business and non-business analytics curricula are semantically distinct, even for topics shared across disciplines, challenging the assumption that non-business courses can easily integrate into business analytics curricula. These distinctions suggest that framing, emphasis and structure shape learner value, making them essential considerations for curriculum design and instructional strategies. The findings underscore challenges in interdisciplinary integration, emphasizing the need for curricular frameworks that maintain both analytical rigor and business relevance. Practical implications Findings highlight the risks of borrowing courses from non-business disciplines without careful evaluation, emphasizing the need for intentional curricular design to ensure alignment with business education priorities and accreditation expectations. Originality/value This study is among the first to apply machine learning and NLP to examine structural and semantic differences across data analytics disciplines. Beyond topic-based comparisons, it demonstrates that business analytics education differs in how analytics is framed and taught.