Identifying patterns in time series data, inference, estimation, prediction, general properties of time series models, systematic pattern and random noise, trend and seasonality analysis, time domain and frequency domain analysis, data visualization, linear and mixed models, AR models, ARMA models, ARIMA models, identification and parameter estimation, model estimation and forecasting, Akaike information criterion, mixed models, single spectrum and cross spectrum analysis, higher order statistics, state space models, Kalman filter, non-Gaussian linear models, Generalized autoregressive conditional heteroskedastic (GARCH) models, stochastic volatility models, extreme value theory, nonlinear time series models, applications in data science.