Multilevel regression and poststratification (MRP) is a method of estimating granular results from higher-level analyses. While it is generally used to estimate survey responses at a more granular level (e.g., by academics in political science to estimate state-level opinion from national polls), MRP has clear applications in industry-level data science. Rumman Chowdhury reviews the methodology behind MRP and provides a hands-on programming tutorial.
Rumman Chowdhury is a senior manager and AI lead at Accenture, where she works on cutting-edge applications of artificial intelligence and leads the company’s responsible and ethical AI initiatives. She also serves on the board of directors for three AI startups. Rumman’s passion lies at the intersection of artificial intelligence and humanity. She comes to data science from a quantitative social science background. She has been interviewed by Software Engineering Daily, the PHDivas podcast, German Public Television, and fashion line MM LaFleur. In 2017, she gave talks at the Global Artificial Intelligence Conference, IIA Symposium, ODSC Masterclass, and the Digital Humanities and Digital Journalism conference, among others. Rumman holds two undergraduate degrees from MIT and a master’s degree in quantitative methods of the social sciences from Columbia University. She is near completion of her PhD from the University of California, San Diego.
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