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Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula-early and often-will help statisticians be part of the dialogue regarding Big Data and Big Questions.
The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completio
The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the primary steps to generate high quality training data, most of the fai
At a time when many companies are under pressure to reduce times-to-market the management of product information from the early stages of design through assembly to manufacture and production has become increasingly important. Similarly in the constr
In network analysis, many community detection algorithms have been developed, however, their implementation leaves unaddressed the question of the statistical validation of the results. Here we present robin(ROBustness In Network), an R package to as
Cloud platform came into existence primarily to accelerate IT delivery and to promote innovation. To this point, it has performed largely well to the expectations of technologists, businesses and customers. The service aspect of this technology has p