Increased need for data analytics education in support of verification and validation

Computational simulation studies utilize data to assist in developing models, including conducting verification and validation (V & V). Input modeling and V & V are, historically, difficult topics to teach and they are often only offered as a cursory introduction, leaving the practitioner to pick up the skills on the job. This problem of teaching is often a result of the inability to introduce realistic datasets into class examples because “real world” data tends to come in poorly formed datasets. In this paper, a case is made that teaching data analytics can help ease this problem. Data analytics is an approach that includes data wrangling, data mining, and exploratory analyses through visualization and machine learning. We provide a brief discussion on how data analytics has been be applied to computational modeling and simulation in the context of verification and validation.

Recommended citation: Lynch, Christopher J; Gore, Ross; Collins, Andrew J; Cotter, T Steven; Grigoryan, Gayane; Leathrum, James F. (2021). "Increased need for data analytics education in support of verification and validation." 2021 Winter Simulation Conference (WSC). 1-12.
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