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1 - 1 of 1 results for: COMM 382: Big Data and Causal Inference

COMM 382: Big Data and Causal Inference

Massive datasets of text, images, video, so-called big data, are increasingly available for research because of the pervasive adoption of new information communication technologies such as social media. These data represent new opportunities for social science research, but prominent examples of big data and data science bear little resemblance to the research designs of social scientific inquiry for causal inference. In this course, we harness the power of big data for causal inference by using machine learning and statistical tools on large-scale digital media datasets to answer social science questions of cause and effect. Prerequisite: Ph.D. student or consent of instructor; students should have taken quantitative methods and be willing to learn programming. Familiarity with Python recommended.
Terms: Win | Units: 1-5
Instructors: Pan, J. (PI)
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