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ABSTRACT

Graduate programs in data analytics differ not only in content but also in how they frame the purpose of analytics education. This study investigates whether business and non-business programs exhibit distinct curricular framing orientations. It hypothesizes that business programs emphasize outcome-oriented framing, presenting analytics as a tool for decision-making and value delivery, while non-business programs emphasize mastery-oriented framing focused on technical depth and methodological rigor. To evaluate this distinction, the study analyzes 1,972 course descriptions from 109 graduate programs using natural language processing techniques capable of identifying semantic similarities in instructional language. The results reveal significant differences in how programs describe the role of analytics, with statistical and validation analyses supporting the hypothesized framing distinction. These findings position framing orientation as a structural feature of curriculum design and provide practical guidance for educators, curriculum developers, and academic leaders seeking to align analytics programs with institutional goals and workforce expectations.

KEYWORDS:

Data Analytics Curriculum, Framing Orientation, Graduate Education, Business Analytics Programs, Semantic Modeling, Curriculum Design Patterns, Natural Language Processing, Course Description Analysis

Introduction

As organizations increasingly rely on data to inform decision-making, the demand for professionals who can translate information into actionable insight continues to grow. Graduate programs in data analytics have emerged as a key pipeline for developing this workforce, equipping students with technical capabilities, analytical thinking, and domain-specific knowledge. Among these offerings, business analytics has evolved into a distinct area that integrates quantitative methods with managerial insight to address organizational challenges.

Designing effective data analytics curricula requires more than assembling a list of technical topics or mapping competencies to industry roles. Educators must respond to a complex set of pressures: aligning with accreditation standards, incorporating both business and technical knowledge, managing credit hour limitations, and accommodating varied faculty expertise(Gharehgozli, Gupta, & Seung-Kuk, 2024; Gupta, 2023). These constraints shape not only what is taught but also how programs structure learning experiences and frame the purpose of analytics education.

The challenge is not unique to business programs. Fields such as healthcare, engineering, and the social sciences also integrate data analytics into their graduate education, often drawing on similar tools and techniques. However, these disciplines differ in how they frame the role of analytics within their professional domains. For instance, healthcare analytics frequently emphasizes clinical outcomes...

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