Computational Thinking is a process seldom taught at Communication or Social Sciences Faculties. It involves the formulation of problems so that the proposed solutions are transformed into computational steps and algorithms. This set of problem-solving methods is key in a rapidly evolving technological landscape. That is why, the Communications faculty of a Peruvian university introduced in 2024 a new and mandatory 16-week course in Computational Thinking tailored to Communication students. They learn theoretical concepts which are then translated to Python code in order to create interactive projects that use data related to their career.

This qualitative study presents the findings after a year of progress. The sample size consisted of 84 students across 4 different classrooms throughout 2024. They belonged to four different undergraduate careers: Advertisement, Social Development, Journalism, Audiovisual Studies. A Systematization of experiences methodology was employed, which included participant observation and focus groups. The main categories that were explored were Challenges, Opportunities, interdisciplinary insights and their perceptions about programming and Digital communications.

Students found that computational thinking proved useful to structure communication projects. It gave them the tools to create compelling stories using data. The course was, however, not without difficulty. Every classroom had students from different careers, which meant different learning backgrounds. Advertisement students had an edge when dealing with data crawling and Social Development students excelled in understanding data structures. The steep learning curve took a toll on 75% of the students, who felt their progress was slow compared to the speed of themes presented in class. They struggled to connect concepts such as abstraction and pattern recognition to the actual python code, and some of them realized that they had overestimated their basic Digital Competences, which were crucial to quickly acquire complex skills such as programming. The course is being tweaked according to the feedback, introducing Teacher Assistants and an early focus on the final project, which involves selecting, curating, processing and presenting data using programming and storytelling tools. Though the sample size is limited, the feedback and the way students overcame the presented challenges can help educators and researchers bridge the gap when creating interdisciplinary curricula, as well as teaching programming to student in areas related to Liberal Arts, Humanities or Social Sciences.

Keywords: Undergraduate Students, Communication, Programming, Computational Thinking, Qualitative Studies, Focus Groups, Participant Observation.