Problem-Based Learning with the Interdisciplinary Academic and Industrial Projects for the Practical Data Analysis Course

The practical data analysis course covers broad range of technical foundations and interdisciplinary data analytical cases. However, students selecting this course often have varied learning background and limited practical experiences in interdisciplinary studies. To address the challenges, we propose a novel educational model, named as the problem-based learning with the interdisciplinary academic and industrial projects (IAIP-PBL). The proposed model incorporates four curriculum reform measures, including academic and industrial projects, project-guided stepped course content, interactive coding platform Canis, and self-learning activities. Through those curriculum reform measures, the course has obtained high student satisfaction rate and gained positive feedback from peer universities. The proposed learning model demonstrates strong generalizability and can be widely applied.