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Accelerating data-informed decision-making
Vision
To create an internationally recognized hub of educational, behavioral, and social data scientists by advancing the use of data to inform social system planning and decision-making.
Mission
To accelerate data-driven research and outreach efforts in the educational, behavioral, and social sciences, promote collaboration, and train future leaders in social data science methods.
Announcements
November 2021: Faculty Data Science Fellow Seed Funding Awards announced.
We are pleased to announce the following Faculty Fellows who have been awarded seed grants to support their data science work in 2022:
- Brian Gearin and Jessica Turtura, “Understanding State-Level Dyslexia Legislation through Natural Language Processing”
- María Reina Santiago-Rosario, “Identifying Pivotal Teacher Instructional Behaviors to Achieve Racial Equity in School Discipline”
- Keith Zvoch, “Predicting 9th Grade On-Track to Graduation Status with Tree-Based Variants of the Semi-Parametric Stochastic Mixed Effects Model”
Upcoming Events
A Decade in Data Science: Highlights from Ed Tech to Big Tech
Dr. Peter M. Crosta, Google
Friday April 29th, 11am-12pm PT via Zoom
Though formally trained as an education economist, Peter Crosta has been calling himself a data scientist for at least a decade. In this talk, he’ll discuss some of the problems he’s tackled from academia to industry and how data science methods and tools supported the efforts. The talk will conclude with a discussion of some useful frameworks that can be combined with data science for conducting user experience research.
Bio:
Dr. Peter M. Crosta is a Senior Quantitative User Experience Researcher on the Travel team at Google who is always excited to talk about quantitative research and data science. He spent his early career studying postsecondary education at Columbia University’s Teachers College, has led data science teams at two education technology startups, and has consulted for several Fortune 500 companies. He is an avid R user and is currently studying Bayesian statistics. Peter holds a BA in economics from Cornell University and a PhD in economics and education from Columbia University.
RSVP here
The Future of Data Science: Automation, Accessibility, and Accountability
Kelsey Gray, Insight Policy Research
Monday, June 6th, 11am-12pm PT via Zoom
Ask 10 data scientists to define “data science” and you’ll receive 10 different answers. Data science is an interdisciplinary field that is constantly evolving. However, three core characteristics define the future of this field: automation, accessibility, and accountability.
This talk describes how Federal and State agencies, non-profits, and foundations are using automation, expecting accessibility, and demanding accountability when it comes to the application of data science and analytic techniques. Examples are presented that demonstrate:
- how natural language processing techniques and report automation are routinely deployed to expedite the flow of information and insights;
- how data visualizations and interactive dashboards are making those results more accessible for decision makers and improving data literacy; and
- how users expect accountability when it comes to the use, transparency, and replicability of machine learning algorithms. Successful data science projects seamlessly integrate automation, accessibility, and accountability.
Bio:
Kelsey Gray is an associate director of Data Science and Analytics at Insight Policy Research (Insight). She brings 10 years of experience assessing social programs and policies, with a specialty in data visualization and advanced analytics. She works with Federal agencies, State governments, non-profits, and foundations to make data more accessible for decision-making. Kelsey leverages her formal training in data visualization and design principles to convey complex information in easy-to-understand formats for the end user. She currently directs projects deploying interactive dashboards for government agencies and foundations, leads work to develop machine learning algorithms to classify unstructured data, and oversees tasks to enhance the data literacy of clients and organizations. Prior to joining Insight, she was a learning engineer at Project Evident, where she led work with nonprofit service providers to implement data science techniques and dashboards. As an analyst at Mathematica Policy Research, she used simulation models to estimate the impact of policy changes on social programs. Kelsey holds an M.S. in analytics from New York University and a B.A. in public policy from the University of North Carolina at Chapel Hill. She is a certified Tableau Specialist.