Data Modeling for Undergraduate Data Science

Abstract

Widespread interest in developing and enhancing undergraduate data science education is evidenced by the interim report Envisioning the Data Science Discipline: The Undergraduate Perspective recently released by the National Academies of Science/Engineering/Medicine. The report identifies data modeling as one of the key concepts for developing and applying data acumen (making good decisions and judgements with data). We define data modeling as a process for documenting how data is connected, processed, and stored; a particular data model may address one or more of these aspects. This BoF session is proposed to focus specifically on data modeling skills for data science study. Traditional data models taught in computing curriculum include Entity-Relationship, UML, and relational models; these topics can be covered at various points in the curriculum, but are typically included in database courses. The proliferation of advanced data models and systems (for example, but not limited to, document stores, graph databases, column stores, key-value stores, and relational + map reduce) provides an opportunity for developing/enhancing database curriculum at the undergraduate level to support programs in data science. A goal of this BoF is to identify faculty who wish to develop and share best practices for teaching data modeling for data science, including course learning objectives and outcomes, techniques, and materials.