This paper presents an integrated learning approach implemented in the Time Series module designed for Data Science students at ESPRIT School of Engineering. Crafted within the CDIO framework, the approach consists of designing and implementing end-to-end solutions for Time Series forecasting at advanced level. It addresses real-world challenges while prioritizing additional learning objectives that promote the development of personal and interpersonal skills, including collaboration, critical thinking, ethical awareness, and other relevant abilities. To ensure the grasp of theoretical and practical aspects of Time Series fundamentals, students are divided into working groups to actively engage them in their learning process through the integration of three key elements adhering to the objectives of an Integrated Learning Experience; the adoption of the Golden Circle framework, the utilization of the CRISP-DM work methodology, and the consideration of the 17 Sustainable Development Goals (SDGs). The approach delivery consists of a presentation that emphasizes all project phases. Assessment tools are designed to evaluate the methodology of work, the delivered cognitive output in addition to personal and interpersonal skills. The validation of the approach is equally reliant on students’ feedback as a valuable tool. Formative and Summative evaluations of this approach have shown promising results, with students exhibiting increased confidence, competence and awareness in applying mathematical concepts to real-world scenarios. All things considered, this approach is applied to one class of students among three, and comparing final exam grades revealed an improved cognitive learning experience compared to the other two classes.
