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Back to School With Data for Good

1 September 2019 No Comment
This column is written for those interested in learning about the world of Data for Good, where statistical analysis is dedicated to good causes that benefit our lives, our communities, and our world. If you would like to know more or have ideas for articles, contact David Corliss.

David CorlissWith a PhD in statistical astrophysics, David Corliss leads a data science team at Fiat Chrysler. He serves on the steering committee for the Conference on Statistical Practice and is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods in issue-driven advocacy.

With the start of the academic year, students, professors, and staff are looking forward to the opportunities the new year brings. One order of business many students face—and sometimes delay as long as possible—is choosing a subject for term papers, class projects, and other research. To turn this potentially worrisome task into a unique opportunity, students might consider how Data for Good subjects can meet the educational specifications given by their professors. This can create an opportunity for an interesting paper or project that addresses a question of need in the community and develops D4G experience while completing class requirements.

While the goal of the class is learning new analytic methods, there can often be a fair amount of latitude in how these methods are applied: The techniques are determined by the class, but the specific cases may vary widely. Programming courses can find practical applications in any subject to make a difference in an area students care deeply about. A project for a big data or machine learning class can mine social media and extract features. Professors can inspire students with projects that make the grade and make a difference. D4G projects can facilitate team projects by choosing a subject that motivates the group to work together and go above any course requirements.

Of course, student papers in Data for Good aren’t just for STEM courses. Classes outside the sciences can benefit from scientific research. Here, the context is often the reverse of science classes: The subject is narrowly defined by the class but the research methods vary widely. For example, document vectorization and feature extraction of Abraham Lincoln’s speeches could be plotted on a time series—like a search on Google Trends—to trace the development of his thought and language from opposing the expansion of slavery, to “send them back” to Africa or Central America, to emancipation, to reconciliation in the second inaugural address … a gradual development reflected in our wider society today.

Good statistical science and a little creativity can go a long way toward developing both your analytic skills and D4G practice while delivering a powerful, creative, and unique essay to professors tired of reading term papers that merely regurgitate lecture points. Of course, it’s always important to read the syllabus carefully and discuss ideas for papers and class projects with your professors to be sure they meet the requirements for the course. I think most professors would be only too happy to see modern methods of text analytics, econometrics, and other statistical research techniques applied to literature, history, philosophy, and the arts.

Outside the Classroom

Beyond classes, there is an abundance of educational opportunities in Data for Good. Hackathons are a great way to expand your skills and horizons at the same time. The ASA’s DataFest is one part hackathon and one part Kaggle competition, with events at a number of universities over a weekend. Multiple small teams at each site explore the same data set over several weekends late in the school year, with applications to host an event likely due in January.

Another ASA activity is the annual Data Challenge. The 2019 Data Challenge looked at data from the New York City Housing and Vacancy Survey. Next year’s JSM has a D4G theme, so the Data Challenge should be a good opportunity to get involved. More information about the 2020 event will be available around the end of the year. Keep watching this column to find out more.

Many organizations have resources to support projects and events in which students can participate. The AAAS Science and Human Rights Commission offers a Campus Event Toolkit that includes guidance and materials for events and ways to apply science to human rights research. Next year is a census year, and the US Census Bureau’s Statistics in Schools program provides resources for K–12 activities that could be implemented by college students in statistics education. Teach Data Science maintains a list of Data for Good groups, each full of opportunities for student and faculty research. (If you are part of a D4G organization, look into getting connected here!)

Internships and research partnerships with industry are another way students can become involved in Data for Good projects while at school. Whether scouting places to work after graduation or just trying new experiences, Data for Good can be on your list of considerations.

Students, professors, and research partners can all find ways the important work of Data for Good intersects with course requirements and other educational activities. Finding your project for next year will bring new opportunities for learning and service. It also might be the beginning of a lifetime of activity in Data for Good.

The theme for next year’s JSM is “Everyone Counts: Data for the Public Good”! Watch this space for more information as statistics’ biggest event in North America focuses on Data for Good. Now is the time to organize topic-contributed session proposals. I’m putting together one on human rights analytics (i.e., genocide forecasting) and doing what I can to help develop other D4G proposals. Let me know if I can help refine an idea, suggest possible speakers to complete a proposal, or facilitate in other ways.

Get Involved

Throughout the school year, many organizations host events to encourage girls and young women to develop their careers, often in STEM fields. One such program is Dream It, Be It from Soroptimist International. This particular program focuses on girls with multiple barriers to career development. I can write about this vitally important work by Soroptimists and others but can never do what is needed most, which is to be the role model these young women need. Through programs such as this, professional women in statistics and data science make a huge impact on the future of Data for Good by making a difference in the lives of girls at a critical point in their development.

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