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The Hitchhiker's Guide to Data Science for Social Good
| Date | Stars |
|---|---|
| 2026-07-24 | 1050 |
| 2026-07-25 | 1051 |
| 2026-07-28 | 1051 |
| 2026-07-30 | 1051 |
| 2026-08-06 | 1051 |
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# Welcome to the Hitchhiker's Guide to Data Science for Social Good. ## What is the Data Science for Social Good Fellowship? The [Data Science for Social Good Fellowship (DSSG)](http://dssgfellowship.org) is a hands-on and project-based summer program that launched in 2013 at the University of Chicago and has now expanded to multiple locations globally and is currently coordinated by the [Data Science for Social Good Foundation](http://www.datascienceforsocialgood.org) and [Carnegie Mellon University](http://www.datasciencepublicpolicy.org). It brings a group of fellows, typically graduate students (or senior undergraduate students in some cases), from across the world to work on machine learning, artificial intelligence, and data science projects that have a social impact in partnership with social good organizations. From a pool of typically around 1000 applicants, 20-40 fellows are selected from diverse computational and quantitative disciplines, including computer science, statistics, math, engineering, psychology, sociology, economics, and public policy. The fellows work in small, cross-disciplinary teams on social good projects spanning education, health, energy, transportation, criminal justice, social services, economic development, and international development in collaboration with global government agencies and non-profits. This work is done under close and hands-on mentorship from full-time, dedicated,senior data science mentors as well as dedicated project and partnership managers, with industry and/or government experience. The result is highly trained fellows, improved data science capacity of the social good organization, and a high-quality data science project that is ready for field trial and implementation at the end of the program. In addition to hands-on project-based training, the summer program also consists of workshops, tutorials, and ethics discussion groups based on our data science for social good curriculum designed to train the fellows in doing practical data science and artificial intelligence for social impact. ## Who is this guide for? The primary audience for this guide is the set of fellows coming to DSSG but we want everything we create to be open and accessible to the larger world. We hope this is useful to people beyond the summer fellows coming to DSSG. **If you are applying to the program or have been accepted as a fellow,** [check out the manual](dssg-manual/) to see how you can prepare before arriving, what orientation and training will cover, and what to expect from the summer. **If you are interested in learning at home,** check out the [tutorials and teach-outs](curriculum/) developed by our staff and fellows throughout the summer, and to suggest or contribute additional resources. *Another one of our goals is to encourage collaborations. Anyone interested in doing this type of work, or starting a DSSG program, to build on what we've learned by **using and contributing to** these resources. ## What is in this guide? Our number one priority at [DSSG](http://dssgfellowship.org) is to **train fellows to do responsible data science/ML/AI for social good work**. This curriculum includes many things you'd find in a data science course or bootcamp, but with an emphasis on solving problems with social impact, integrating data science with the social sciences, understanding and discussing ethical implications of the work, as well as privacy and confidentiality issues. We have spent many (sort of) early mornings waxing existential over Dunkin' Donuts while trying to define what makes a "data scientist for social good," that enigmatic breed combining one part data scientist, one part helper, one part educator, and one part bleeding heart idealist. We've come to a rough working definition in the form of the skills and knowledge one would need, which we categorize as follows: - **Programming,** because you'll need to tell your computer what to do, usually by writing code. - **Compute
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f1493dd00919634d, topic:training