Context, Data, and Assumptions: Their Roles in Mathematical Modeling and Data Science

Abstract

In this conceptual paper, we share the intersections of mathematical modeling and data science through examining the role of context, data, and assumptions within these mathematical processes. We argue, in modeling context is central to decontextualizing and recontextualizing, while in data science it drives creation and connection. In modeling, assumptions guide model formulation, but must be checked before choosing analytical methods in data investigations. In modeling, data motivates and validates models, whereas they permeate every stage of the data science cycle. We discuss three major contributions: (1) a theoretical perspective for distinguishing between modeling and data science; (2) implications for modeling and data science task design within a shared context, and (3) implications for teacher-education.

Keywords

mathematical modelingdata sciencecontextassumptionsdata

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