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Data Science for Social Good fellows partner with Ushahidi
By Rob Mitchum
“2-car acc @ State & Lake, both drivers injred”
That short, hastily typed text message or tweet contains a lot of information that police, emergency responders, news organizations and drivers could use. A human observer could quickly identify that it refers to an auto accident, a medical emergency, and a street intersection in Chicago. But without prior experiences and lots of human input, a computer would likely have a hard time recognizing that State and Lake are streets in Chicago, that “acc” is short for accident, or that “injred” is a typo for “injured.”
Computer science offers machine learning and natural language processing techniques that can make sense of messy and disorganized text. Those techniques are at the heart of one of the summer projects of the Data Science for Social Good fellowship. (A University of Chicago program funded by Google’s Eric Schmidt and run by former Obama campaign chief data scientist Rayid Ghani, now at the Computation Institute. To learn more about the fellowship check out the website or read this previous post in the series). Working with the non-profit organization Ushahidi, a team of three fellows hopes to accelerate the processing of incoming messages during disasters, contested elections and other crises to quickly spread information and mobilize responses.
Chicago-Based Data Science for Social Good Fellows Focus on Problem Solving
Data science isn’t just about creating algorithms, writing code, or visualizing data. The first step is finding the right problem to solve.
Many of the governments and nonprofit organizations we’ve talked to while developing the Data Science for Social Good fellowship at the University of Chicago are excited about using data to make better decisions. (The fellowship is funded by Google’s Eric Schmidt and run by former Obama campaign chief data scientist Rayid Ghani, now at the University of Chicago’s Computation Institute. To learn more about the fellowship check out the website or read this previous post in the series.) But most aren’t quite sure where to start, while others pitch lots of problems that are initially too vague to solve with data. To help these organizations grow their impact, data scientists must be hands on. They need to quickly learn the ins-and-outs of unfamiliar fields, from health care to energy to municipal government. They need to understand what data is available both inside and outside an organization, and a knack for distilling ill-defined problems into clear and tractable ones.
Training Aspiring Data Scientists in Chicago
As technology penetrates further into everyday life, we’re creating lots of data. Businesses are scrambling to find data scientists to make sense of all this data and turn it into better decisions.
Businesses aren’t alone. Data science could transform how governments and nonprofits tackle society’s problems. The problem is, most governments and nonprofits simply don’t know what’s possible yet. There are too few data scientists out there and too many spending their days optimizing ads instead of bettering lives. To make real impact with data, we need to work on high-impact projects that show these organizations the power of analytics. And we need to expose data scientists to the problems that really matter.
That’s exactly why we’re doing the Eric and Wendy Schmidt Data Science for Social Good summer fellowship at the University of Chicago. The program is led by Rayid Ghani, former chief data scientist for the 2012 Obama campaign, and is funded by Google Chairman Eric Schmidt.
We’ve brought three dozen aspiring data scientists from all over the world to Chicago to spend a summer working on data science projects with social impact. The fellows are working closely with governments and nonprofits (including the City of Chicago, the Chicago Transit Authority, and the Nurse-Family Partnership) to take on real-world problems in education, health, energy, transportation, and more. (To read up on our project, check out dssg.io/projects or to get involved, go to github.com/dssg.)
Data scientists are a hybrid group with computer science, statistics, machine learning, data mining, and database skills. These skills take years to learn and there’s no way to teach all of them during a few weeks. Instead of starting from scratch, we decided to start with students in computational and quantitative fields – folks that already have some of these skills and use them daily in an academic setting. And we gave them the opportunity to apply their abilities to solve real-world problems and to pick up the skills they’re missing.