Our number one priority at DSSG is to train fellows to do data science for social good work. To this end, we've put together this curriculum, which includes many things you'd find in a data science course or bootcamp, but includes an emphasis on social science, ethics, privacy, and social issues.
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.
If you are interested in learning at home, check out the tutorials and teach-outs developed by our staff and fellows throughout the summer, and to suggest or contribute additional resources.
Another one of our goals is to cultivate interest in and encourage collaboration between the data sphere and the social sphere. We invite anyone else 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.
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 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, and sometimes even do causal inference.
- Scoping and project management, because you'll need to be able to talk to your partners about how 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 and security, because data is people and needs to be kept secure and confidential.
- Ethics, fairness, bias, and transparency, because your work has the potential to be misused or have a negative impact on people's lives, so you have to consider the biases in your data and analyses, the ethical and fairness implications, and how to make your work interpretable and transparent to the users and to the people impacted by it.
- 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.
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