Welcome to the Hitchhiker's Guide to Data Science for Social Good.
Our number one priority at DSSG is to train fellows to do data science for social good work. This curriculum includes many things you'd find in a data science course or bootcamp, but with 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 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 Data Science for Social Good?
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.
- Problem and Project Scoping, because you'll need to be able to go from a vague and fuzzy project description to a problem you can solve, understand the goals of the project, the interventions you are informing, the data you have and need, and the analysis that needs to be done.
- Project management, to make progress as a team, to work effectively with your project partner, 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.
Table of Contents
The links below will help you find things quickly.
This sections covers general information on projects, working with partners, presentations, orientation information, and the following schedules:
- High level summer plan: details what the goals are for each week of the program
- Orientation schedule: detailed schedule for the first two weeks of the program
This section details the DSSG anti-harassment policy, goals of the fellowship, what we hope fellows get out of the experience, the expectations of the fellows, and the DSSG environment. A slideshow version of this can also be found here.
This section details the various topics we will be covering throughout the summer. This includes:
- Getting Started
- Getting and Keeping Data
- Data Exploration and Analysis
- Modeling and Machine Learning
- Programming Best Practices
- Presentations and Communications
- Tutorial Templates (for making your own tutorials!)
In the wiki, you will find a bunch of helpful information and instructions that people have found helpful along the way. It covers topics like:
- Accessing S3 from the command line
- Creating an alias to make Python3 your default (rather than python2)
- Installing RStudio on your EC2
- Killing your query
- Creating a custom jupyter setup
- Mounting box from ubuntu
- Pretty Print psql and less output
- Remotely editing text files in your favorite text editor
- SQL Server to Postgres
- Using rpy2
- VNC Viewer