Delivering a Business Analytics Course Focused on Data Mining for Both Technical and Non-Technical Students

Abstract

The current study details the development of an undergraduate business analytics course that combines components of both active and experiential learning. The course offering is designed to expose students from different backgrounds to an intermediate-to-advanced level of business analytics. The course is unique in that it was designed to be appropriate for both “tech savvy” and non-technical learners—two groups who likely possess very different skill sets. The course incorporates high-level analytic techniques and algorithms that enhance decision-making and makes use of a business analytics platform called RapidMiner that includes embedded analytic frameworks, so learners do not require prior computer programming experience to be successful. Each course module incorporates different types of lab projects—including heavy usage of guided lab projects, self-paced problem-solving labs, and exam-based lab assessments—where students have multiple opportunities to practice building increasingly sophisticated experiences over time. Pre-and post-course surveys were used to assess course design, including student engagement, student learning, learning interest, and learning satisfaction. Quantitative analyses of course perceptions over time reveal that students, on average, report increases in engagement, satisfaction, and learning interest. Students demonstrate significant improvements in their understanding and overall attitudes toward business analytics, which appears to generate additional excitement about future exposure to business analytics as a subject of interest.