Unleashing the Problem-Solving Potential of Next-Generation Data Scientists

Data science, an emerging multidisciplinary field, resides at the intersection of computational sciences, statistical modeling, and domain-specific sciences. The current norm for data science education predominantly focuses on graduate programs, which presume a preexisting knowledge base in one or more relevant sciences. However, this framework often overlooks those who don’t plan to pursue graduate studies, thereby limiting their exposure to this rapidly expanding field. Penn State addressed this gap by establishing one of the first undergraduate degree programs in data sciences, a collaboration between the College of Information Sciences and Technology, the Department of Computer Science and Engineering, and the Department of Statistics. One key component of this program is a project-focused, writing-intensive course designed for upper-class undergraduates. This course guides students through the entire data science project pipeline, from prob-lem identification to solution presentation. It allows students to apply foundational data science principles to real-world problems, advancing their understanding through practical application. This chapter details the objectives, rationale, and course design, alongside reflections from our teaching experience. The insights provided could be helpful to instructors developing similar data science programs or courses at an undergraduate level, broadening the influence of this important field.