Welcome to the Hitchhiker's Guide to DSSG.
We at DSSG 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 consultant, 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.
- Computer science, because you'll need to understand how your data is - and should be - structured, as well as the algorithms you use to analyze it.
- Math and stats, because everything else in life is just applied math, and numerical results are meaningless without some measure of uncertainty.
- Machine learning, because you'll want to build predictive or descriptive models that can learn, evolve, and improve over time.
- Social science, because you'll need to know how to design experiments to validate your models in the field, and to understand when correlation can plausibly suggest causation.
- Scoping and project management, because you'll need to be able to talk to your partners about how to the data they have relates to the problem you're trying to solve, come up with a useful solution, and work with a team to make that useful solution actually happen.
- Privacy, ethics, and security, because data is people, so you'll need to consider the potential effects that using data will have on the people it represents, as well as keep potentially sensitive data secure.
- Communications, because you'll need to be able to tell the story of why what you're doing matters and the methods you're using to a broad audience.
- Social issues, because you're doing this work to help people, and you don't live or work in a vacuum, so you need to understand the context and history surrounding the people, places and issues you want to impact.
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. So we've put together a variety of resources for before, during, and after the summer. If you are applying to the program or have been accepted as a fellow, check out the manual to see how you can prepare before arriving, what orientation and training will cover, and what to expect from the summer. We invite everyone to follow along with the tutorials and teach-outs prepared by our staff and fellows throughout the summer, and to suggest or contribute additional resources.