For Government Agencies, Non-Profits, and Other Organizations:

Our trainings for governments and non-profits are designed for:

  • Directors and Executives who want to understand what data science can do for governments and non-profits, how to build effective teams, what to watch out for when working with vendors and consultants, and how to get started with a data-driven policy and social impact project.
  • Analysts and Policymakers who are interested in getting hands-on experience with applying data science concepts to problems they face in their organizations.

Executive Training Program in Applied Data Analytics for Public Policy

DSaPP has joined with New York University and the University of Maryland to teach professionals key computer science and data science skills for public policy. The program includes classes on applied problems, such as “Working with Data on Welfare Recipients” and “Working with Data on Veterans.” Find more information at the Executive Program in Applied Data Analytics website.

For Students:

The Data Science for Social Good Fellowship (DSSG)

The DSSG Fellowship is a University of Chicago summer program to train aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Fellows of the DSSG work with governments, non-profits, and mentors from both industry and academia to work on real-world problems in education, health, energy, public safety, transportation, economic development, international development, and more. Over the course of the three months the Fellows spend in Chicago, they build analytical and coding skills as they apply to data science through a combination of seminars and project experience.

The 2016 program brought 42 aspiring data scientists from across the world. This group of highly qualified learners completed 12 projects in the course of 13 weeks. The 4-year history of the program has trained 168 fellows and completed 50 projects in partnership with governments, non-profits, and social enterprises. More information about the exciting work of our DSSG Fellows can be found here.

Data Science for Social Good Fellowship Training Materials

Each summer, we host a new cohort of Data Science for Social Good Fellows. Throughout the program, we hold classes and provide materials on programming, computer science, math and statistics, machine learning, social science, project scoping and management, privacy, ethics, security, communications, social issues, and more. We make many of our training materials available on GitHub.

Data Science for Social Good Conference

In 2016, we launched the Data Science for Social Good conference which brings together current DSSG fellows, DSSG alumni, project partners both past and present, and people and content from other DSSG programs. This conference highlighted the successes, opportunities, and challenges faced by the growing Data Science for Social Good community by bringing key members from each community (academia, governments, non-profits, foundations, social enterprises, and corporations) together to share best practices, learn from each other, and generate new collaboration opportunities.

Courses @ UChicago

If you are a student at the University of Chicago and are interested in formal training in data analytics, our director and postdocs currently teach three courses. Please check the University time schedules for the most up-to-date information.

PPHA 30510 – Data Analytics for Campaigns

CAPP 30524 – Machine Learning for Public Policy

PPHA 30530 Computation and Public Policy

Civic Hackathon (aka Scope-a-thon)

DSaPP is committed to partnering with individuals, communities, and researchers in order to find solutions to social and civic challenges. Most recently, we co-sponsored the Civic Hackathon at the Chicago Innovation Exchange which was focused on scoping data-driven social impact projects with governments and non-profits.

If you’re interested in joining team-based learning events like this in the future, please be sure to check back here and our News feed regularly.