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COVID+DS: Key elements of the analytical toolbox for understanding COVID-related data

Jun 30

Tuesday, June 30, 2020 - 4:00pm to 5:00pm

Virtual session

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Presenter

Matthew Hirschey

The ability to make rapid, data-driven decisions is a key component for prioritizing COVID-19 research, treatment, and public health initiative. This session provides an introduction to the emerging field of data science using the R software language, including data analysis and visualization, with a particular focus on its utility for insights in COVID-19. No prior knowledge of data science or computer programming is assumed; laptops are required. Attendees will be provided with COVID-19 dataset examples, and introduced to R packages and code used to examine data. Particular attention will be paid to code interpretation and data provenance methods by learning to generate reproducible data output files. Although specific datasets will be used for analysis in class, this workshop will provide broadly applicable tools to reproducibly analyze and visualize data across a wide continuum. This session is part of the Duke+Data Science (+DS) program virtual series on COVID-19 + Data Science. Please join us for a 8-week series on data science methods with direct applications to the COVID-19 pandemic. Learn from Duke experts about the state-of-the-art in these 1-hour virtual sessions. For more information, please visit https://plus.datascience.duke.edu