Training Data Scientists Through Project-Based Learning

The concepts of innovation, creativity, problem solving, effective communication, autonomy and critical thinking are at the core of becoming a good data scientist. Adapting to new technological resources and tools is also an important skill, which also builds on the curious and inquisitive nature associated with data science, and is fuelled by rapidly changing data science ecosystems in industry. In this regard, Project-based learning (PBL) has clear benefits for engaging students in data science courses. However, the exploratory character of data science projects, which do not start with a clear specification of what to do, but some data to analyse, pose some challenges to the application of PBL. Our aim is to improve students’ data science learning experiences and outcomes through the use of PBL. In this paper, we share our experiences with PBL and present an assessment rubric that focuses on value, innovation and narrative, which can be used as a scaffolding structure for data science courses. Our analysis of a PBL data science course at MSc level, together with data from student surveys, shows how the methodology and rubric align well with the exploratory nature of data science and the proactive, curious, and inquisitive skills required of data scientists.