Time series data arise in various areas, such as agriculture, crime, demography, health, meteorology, economics, and sales, among others. The analysis of these observed data at different time points leads to unique problems in statistical modelling and inference. This course provides a basic understanding of time series visualization, decomposition, regression, exponential smoothing methods, (seasonal) ARIMA models, dynamic regression models, model selection, and validation. Students get the opportunity to enhance their analytical and computer skills with exercises using R.