Spoilers: mostly it's unavailable.

The graph released by the Georgia Department of Public Health. Tap the graph to see it at full size. Pictured above is a graph released a few days ago by the Georgia Department of Public Health, showing the top 5 counties in the state with the most confirmed cases of COVID-19. You’d think that things […]

In the era of Big Data, we’ve come to believe that, with enough information, human behavior is predictable. But number crunching can lead us perilously wrong.

Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources.

All industries are affected by cyberattacks, but how often and to what extent they occur vary greatly by industry type. Industry Type As for frequency,

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vend…

The Traverse Area District Library Statistics Dashboard