This course investigates the dynamic relationships between variables, including analysis of large scale dependent data. It starts with linear relationships between two variables, including distributed-lag models and detection of unidirectional dependence (Granger causality). The dynamic models discussed include vector autoregressive models, vector autoregressive moving-average models, multivariate regression models with time series errors, co-integration and error-correction models, dynamic factor models, and multivariate volatility models. The course also addresses classification (or clustering) of large scale time series, principal component analysis, asymptotic principal component analysis, online recursive estimation, deep neural networks, and machine learning for dependent data. Empirical data analysis is an integral part of the course. Students are expected to analyze many real data sets. Finally, the course discusses forecasting under the current data-rich environment. The main software used in the course includes the MTS and SLBDD packagesin R, but students may use their own software if preferred.