Helping Data Science Students Develop Task Modularity

This paper explores the skills needed to be a data scientist. Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity. Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity. As a first step, based our study, we identified six task decomposition best practices. However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity. We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students

EXPLORING HOW DIFFERENT PROJECT MANAGEMENT METHODOLOGIES IMPACT DATA SCIENCE STUDENTS

This paper reports on a controlled experiment comparing different approaches on how to guide students through a semester long data science project. Four different methodologies, ranging from a traditional “just assign some intermediate milestones” to other more agile methodologies, are compared. The results of the experiment shows that the project methodology used in the classroom made a significant difference in student outcomes. Surprisingly, an Agile Kanban approach was found to be much more effective than an Agile Scrum methodology, which was not one of the leading approaches