This course delves deeper into the computational analytics of Data Science by introducing foundational and advanced methods in Bayesian analysis and causal inference through lectures and hands-on exercises. Topics include probabilistic modeling, regression techniques, propensity scores, difference-in-differences, conditional treatment effects, and advanced methods such as panel data analysis, metalearners, and Gaussian processes. Students will learn to construct, evaluate, and diagnose Bayesian and causal models using tools like PyMC3, Bambi, and metalearners. The course emphasizes practical applications, including addressing biases, leveraging panel data, and extending causal analyses to real-world decision-making. Hands-on exercises reinforce critical concepts, and students will synthesize methods to solve complex problems.
Course Prerequisite(s)
EN.685.648 Data Science