Since 1790, the US Census has been THE source of data about American people, providing valuable insights to social scientists and humanists.  Mapping these data by census geographies adds more value by allowing researchers to explore spatial trends and outliers.  This workshop will introduce three key packages for streamlining census data workflows in R: 

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data.

Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

Participants in this workshop will learn about some of the issues surrounding the collection of health statistics, and will also learn about authoritative sources of health statistics and data. We will look at tools that let you create custom tables of vital statistics (birth, death, etc.), disease statistics, health behavior statistics, and more. The focus will be on U.S. statistics, but sources of non-U.S. statistics will be covered as well.

Whether you need a quick fact or a data set to analyze, this workshop will lead you to relevant data sources.