This course exposes students to the most popular forecasting techniques used across industries. We cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data, as well as methods of evaluating forecasting models. We also cover basic univariate smoothing and decomposition forecasting methods, including moving averages, ARIMA, Holt-Winters, unobserved components models, and various filtering methods (Hodrick-Prescott filter, Kalman filter). Time permitting, we extend our models to multivariate modeling options such as vector autoregressive models (VAR). We also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course uses the R programming language, though no prior experience with R is required.