Integrating Alteryx for Teaching Data Analytics in Low-Computing Programs

In response to the growing need for accessible data analytics education among low-computing disciplines, this study presents the design, implementation, and outcomes of a no-coding graduate-level data analytics course offered within the Engineering Management and Technology Department at the University of Tennessee at Chattanooga. The course utilizes Alteryx Designer 2025.2, an end-to-end, drag-and-drop analytics platform that enables students with minimal programming background to conduct complete data workflows, including data cleansing, transformation, and predictive modeling. Through a project-based learning (PBL) approach, students engage in real-world problem solving, developing data reasoning and interpretation skills rather than focusing on programming syntax. Course artifacts, student project outcomes, and instructional observations suggest that the use of a no-code platform, combined with hands-on assessment through video exercises and mentored projects, supports the development of analytical reasoning, engagement, and data interpretation skills. The paper concludes that GUI-based, no-code tools can effectively bridge the technical accessibility gap in data analytics education, making data-driven learning practical and scalable across low-computing academic programs. This paper is presented as a descriptive pedagogical case study, focusing on course design, instructional practices, and observed learning outcomes rather than a controlled empirical evaluation.