Data science competencies for physics education

Data Science is an emerging field that has witnessed exponential growth and popularity over the past decade. Despite the popularity, there is frequently a disconnect between skills that employers desire and the university curriculum. Moreover, the Data Science job title and its competencies are still not well-defined. Here I will describe our continued efforts at The University of Texas at Arlington (UTA) to bridge the existing gaps between the training of undergraduate students in the Data Science program of UTA and the data-science technical and soft skill competencies that are desired by the job market. We do so by investigating the patterns of required skills, the domain of science, and the characteristics of employers and jobs in the current job market. I will explain how this knowledge leads to the identification of gaps between academic preparation and competencies that employers seek. This continuous gap analysis and feedback can be then dynamically incorporated into the university curricula to ensure the academic programs are aligned well with the needs of society and the job market at all times.