As urban regeneration progressively evolves into a crucial area within the landscape architecture profession, the intricacy and systemic characteristics of such projects necessitate the support of quantitative, accurate, and multi-scalar data-driven research. In the context of new data environments, characterized by big data and open web-based datasets, integrating data-driven methodologies into landscape architecture education is crucial for cultivating leading and innovative professionals. This paper u ses the theoretical course “Urban Regeneration: Theories and Methods” and the design studio “Landscape Architecture Design VI: Urban Landscape Topics” at Soochow University as case studies to propose a collaborative curriculum system centered on research-based design. Through the four stages of perception, discussion, evaluation, and prediction, it analyzes how to bridge research and design within the teaching process effectively. The study further explores how to integrate emerging data-driven techniques into both theoretical and practical instruction in landscape planning and design. Additionally, it examines how course-level integration can augment students’ research and innovation capabilities to more effectively meet the evolving demands of the profession.