Applications in Data Science Webinar Series Recap: Data Science For Social Good
The Concept and Promise of Data Science for Social Good
How to Apply Data Science for Social Good
- It takes a village. A successful data science project for social good require a problem statement, datasets, data scientists, social actors, subject matter expertise, and funding.
- Start with the question, not the data. Answering the right questions is crucial. “What keeps you up at night?” he asked. “How can we use data to answer the questions? How can we identify what we need to know in order to be more effective?”
- Forget “big” data. Size (mostly) doesn’t matter. Bigger isn’t always better. Think more about quality versus quantity. Collect novel data and link it to create interesting relationships.
- Good data science follows good design. In the end, the primary motivation is adoption. “Beyond data and math, we want people to take action, which requires a human-centered approach,” he said.
- These tools have power; we have responsibility. When it comes to humanity, it’s more than producing data, it’s about making a difference. “When we fail in our work, people get hurt.”
Become Part of the Journey
- The American Red Cross uses open data to prevent deaths and injuries from house fires.
- GiveDirectly uses satellite imagery and machine learning to identify and serve families who live below the poverty line.
- Crisis Text Line uses text data to help teens in crisis and increase response times in potentially harmful situations.