This course provides a hands-on introduction to the wide variety of models and techniques used in predictive analytics, including linear and non-linear regression models, classification algorithms, machine-learning techniques like SVM and reinforcement learning, and causal inference. There will be an emphasis on conceptual understanding and interpretation of the models, so that students can interpret the results of these techniques to support effective decision-making. The course will be complemented by many hands-on exercises using the R programming language.