Data science, with the right mix of technical expertise and real-world experience, can be a powerful tool to address pressing social and environmental challenges. This winter, a partnership with the 11th Hour Project continued support for the new Civic Data and Technology Clinic at the University of Chicago, a project-based course that paired teams of student data scientists with organizations working in social and economic justice, sustainability, and climate change. In this webinar, hear from students, faculty, and researchers about their work this quarter in collaboration with Eureka Recycling, Inclusive Development International, and Hohonu. Lightning talks will feature projects including an application to track palm oil deforestation in South Eastern Asia, a system for monitoring recycling logistics and trends in the cities and towns surrounding Minneapolis, a data processing pipeline for aggregating water level data across the country, and a project to collate, index, and serve data on international development finance around the world.
The Civic Data and Technology Clinic and this event are supported by the Harris School for Public Policy, the Center for Data and Computing, and the Master of Science in Computational Analysis & Public Policy program.
The Civic Data and Technology Clinic at the University of Chicago partners with public interest organizations, leveraging data science research and technology to address pressing social and environmental challenges. The Clinic also provides students with exposure to real-world projects and problems that transcend the conventional classroom experience including: 1) working with imperfect datasets, applying models and algorithms to real-world data, and navigating security and privacy issues; and 2) communicating results to a diverse set of stakeholders (e.g., industry, public interest, government agencies), and translating information into actionable insights, policy briefs and software prototypes. The Clinic is an experiential project-based course where students work in teams as data scientists with real-world clients under the supervision of instructors. Students will be tasked with producing key deliverables, such as data analysis, open source software, as well as final client presentations, and reports.