University of California, Berkeley 4 credits Data Science Undergraduate Studies
When we learn about the world from data, how much can we rely on the conclusions we draw? How do we know if we could do better? This course will cover the statistical theory required to measure and control our uncertainty when we analyze large and complex modern data sets. We will use mathematical and computational lenses to examine optimality properties and error bounds. Topics include the Bayesian and frequentist paradigms, asymptotic and finite-sample methods, parametric and nonparametric techniques, causality, and multiple testing.
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