We've distilled 4 years of DSSG and DSaPP experience and projects and many (sort of) early mornings spent waxing poetic over Dunkin Donuts into our idea of "what makes a data scientist for social good." This departs from the (already nebulous) definition of a data scientist, placing more weight on the ability to understand social context and to work with partners from nonprofits and government agencies. Here is our blood, sweat, and tears distilled into one Github repo/manifesto: The Hitchhiker's Guide to DSSG.
There's a whole lot to know, so we split it up into 8 categories:
- Programming, because you'll need to tell your computer what to do, usually by writing code.
- Computer science, because you'll need to understand the data structures and algorithms you use.
- Math and stats, because everything else in life is just applied math.
- Machine learning, because you'll want to build predictive or descriptive models, and will want them to learn, evolve, and improve over time.
- Social science, because you need to understand causal inference and experiments.
- Scoping and project management, because you'll need to turn a real world problem into something you can model, and then work with the people on your team and on the ground to be successful.
- Privacy, ethics, and security, because data is people.
- Communications, because you want your work to be implemented and understood.
- Social issues, because you care about people and need to think through the context you're working in.
As the saying goes, when at DSSG do as the cool kids and data witches do. But cool kids and data witches aren't built in a day. We get a lot of questions about what fellows need to apply for the program or before they arrive, and what they can expect to learn over the summer.
If you are coming to DSSG, check out the prerequisites and the 2016 orientation schedule and the accompanying overview. If you won't be joining us this year, we're working on assembling the resources in our outline of skills you need to do Data Science for Social Good.