Podcast from the national radio show Reveal featuring analysis from Civilytics on prison gerrymandering in Wisconsin, which distorts communities' political representation because of residential segregation and disparities in incarceration.
Readings on data analysis, data science, and artificial intelligence. Intended to spark new ideas and prompt critical thinking about organizations' data system design.
Civilytics won second prize (and learned a lot) in this competition focused on automating the combination of police data, census-level data, and other shapefiles for the Center for Policing Equity.
This data simulation package creates realistic synthetic data that allows users to collaborate across agencies without privacy concerns or the need for a data sharing agreement, while writing code that can then be translated back to the original data.
Civilytics provides R training ranging from single workshop introductions to R to advanced R training on spatial data analysis, web application development with Shiny, predictive analytics and more. The 'model and coach' approach is central to many of Civilytics' trainings.
Interactive equity-focused higher education admissions tool that distills dozens of measures of academic preparation into a single college readiness score that can be used to identify clusters of promising students by race/ethnicity and other characteristics.
Delivery of State-provided Predictive Analytics to Schools: Wisconsin’s DEWS and the Proposed EWIMS Dashboard
Situates the Wisconsin Dropout Early Warning System in the context of national models of predictive analytic systems in education, including a focus on the Early Warning Implementation Monitoring System (EWIMS).
The award-winning Wisconsin Dropout Early Warning System was co-developed with educators to ensure that the sophisticated enterprise scale machine-learning provides schools with a timely list of students who should be considered for additional support and attention.
Describes the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades 6-9. Explains how DEWS' publicly available software modules can be applied to new data and outcomes.