Data Science for the Common Good (DS4CG) is a summer program that trains aspiring data scientists to work on real-world problems that benefit the common good. Our teams of computer science Master's students collaborate with nonprofit organizations and government agencies working in public health, education, health and wellness, environmental conservation, and more.
We wrapped up our 2019 projects with a showcase event in October. Read more about it.
Interested in being a sponsor? Click to learn about ways to support Data Science for the Common Good.
Interested in being a partner organization? Click for information on becoming a Data Science for the Common Good partner.
Data Science for the Common Good 2019 Completed Projects
The Charles River Watershed (CRWA) team analyzed 25 years’ worth of water-quality data collected by citizen scientists, to identify patterns and trends in the levels of E. coli, phosphorus, and chlorophyll. The results will inform policies that promote responsible watershed management and a healthy river ecosystem.
The Greater Holyoke YMCA team analyzed membership and program participation data in order to predict membership churn. The results will help predict members at risk of dropping their membership, giving the YMCA the opportunity to proactively assess and respond.
The Massachusetts Department of Public Health (DPH) team aggregated data from disparate sources to develop risk assessment scores at the city/town level. The results will help the DPH deploy resources more effectively and efficiently, in areas that need them most.
The Metropolitan Area Planning Council (MAPC) team worked on enhancing a forecasting technique originally developed at Northeastern University to project scenarios about future populations, enhancing its efficiency, ease of use, and breadth of applicability. The results will help MAPC to better serve the cities and towns that rely on their expertise for municipal planning.
The Nature Conservancy (TNC) team devised a tool using a computer-vision algorithm to automatically detect whether or not a photograph contains an animal. TNC can apply this tool to photographs captured by remote motion-sensitive cameras placed in the wild, in order to monitor wildlife corridors and better guard against animal-vehicle collisions on roadways.
The Springfield Public Schools (SPS) team combined student data with college-enrollment data from a national clearinghouse to identify factors contributing to identify factors contributing to post-secondary school success.The results will help SPS fulfill their mission of graduating students who are college and career ready.