Catalog Course Description

This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

Fall Term

3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week and 1 hours of Instructional experiences requiring special laboratory equipment and facilities per week and 1 hours of The exchange of opinions or questions on course material per week.

Spring Term

3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week and 1 hours of Instructional experiences requiring special laboratory equipment and facilities per week and 1 hours of The exchange of opinions or questions on course material per week.

Statistics 102