Many datasets naturally have a time series component: records collected over time, financial data, biological data signals such as brain waves or blood glucose levels, weather, and seasonal information. Practicing data scientists need to identify when they encounter time series data when to apply suitable techniques. This course will cover the major topics in time series analysis and forecasting (prediction) including stationary and non-stationary models, autoregressive and integrated autoregressive models, models for estimation, and spectral analysis. Different methods of estimation will be leveraged including maximum likelihood, Bayesian, and spectral estimation. These approaches will be applied to real-world datasets, culminating in a complete analysis from end-to-end.