As data and information technologies become increasingly central to modern society, professionals skilled in data exploitation and process optimization are in high demand. This reality has influenced the curricular design of higher education, leading to the emergence of contemporary degrees rooted in the field of computer science. At the Universitat Politècnica de València, the Technical School of Computer Engineering now offers degrees such as Industrial Computing and Robotics or Data Science. These programs often prioritize specialized subjects, sometimes at the expense of foundational courses. This shift can hinder students’ ability to grasp certain fundamental topics and may result in professionals whose performance is impaired by this lack of primary knowledge.

This research focuses on the Data Structures and Algorithms course within the Data Science bachelor’s degree program. Currently, this subject is delivered through theoretical lectures followed by laboratory sessions where students implement programmatic solutions based on their theoretical understanding. However, the mastery of a data structure requires a clear grasp of its motivation, often rooted in computational efficiency. While this concept is typically well understood by computer science students, it is less instinctive for data science students, who in addition may present gaps in essential knowledge such as programming fundamentals.

The recommendations of the Computing Curricula (CC) and the Computer Science Curricula (CSC) emphasize the integration of foundational technical content with methodologies that promote practical and transversal skills. As Data Science, one of the seven disciplines outlined in the CC, continues to grow in importance, these frameworks advocate for active and collaborative learning strategies to prepare students for interdisciplinary challenges. This connection underscores the necessity of redesigning the course to equip students with both technical expertise and adaptive competencies. For that, our article outlines the procedure for designing, implementing, and evaluating the course using the cooperative learning methodology. Each design decision is tied to its associated educational benefit, demonstrating how it promotes active student engagement.

Students are organized into formal working groups, each assigned a specific data structure. At the conclusion of each unit, the assigned group delivers an oral lecture to their peers. These presentations encompass the motivation behind the data structure’s development, its essential operations, key considerations for implementation, and an example of its application. Within the groups, members are assigned roles to ensure positive interdependence. Teamwork sessions are integrated into the course schedule and conducted in class, facilitating direct communication among team members and providing opportunities for instructors to encourage group discussions. These sessions are documented by the students in a logbook, periodically reviewed by the instructor to address potential conflicts, provide personalized feedback, and guide task development. Finally, the student's final evaluation is computed from three sources: individual mark, group mark and class mark. The class mark of a student will be obtained, in turn, from the scores every other student gets in exercises that focus on the data structure that he or she was tasked to teach, thus establishing personal accountability within the classroom.

Keywords: Data science, Data structure, Cooperative learning, Active engagement.