Empowering data science education through low‐code and no‐code analytics platforms: A pedagogical roadmap

The growing demand for data literacy across disciplines has intensified the need for instructional approaches that balance technical rigor with accessibility, engagement, and real‐world relevance. In data science education, code‐centric instruction can support technical depth, but it may also divert novice learners’ attention toward syntax and debugging at the expense of problem formulation, interpretation, and decision reasoning. At the same time, the rise of generative AI has changed this landscape by reducing some syntax‐related barriers while increasing the importance of validation, critique, and responsible workflow design. This paper positions low‐code/no‐code (LC/NC) analytics environments as one component of a broader hybrid pedagogy for teaching analytics and data science. Drawing on cognitive load theory, constructivist learning, and prior classroom and practitioner literature, the paper develops a conceptual roadmap for using visual workflow tools, GenAI supports, and selective coding in complementary ways. Rather than arguing that LC/NC platforms replace programming, the paper proposes that they can serve as effective scaffolds for helping diverse learners engage earlier with analytical reasoning, collaboration, and end‐to‐end workflow thinking. The paper concludes with practical implications for educators seeking to design more inclusive and pedagogically coherent data science curricula.