This course provides a comprehensive introduction to statistical techniques for analyzing discrete response data and time series. It covers methods for modeling and interpreting categorical outcomes, such as logistic regression and Poisson regression, alongside techniques for understanding temporal dependencies in data, including autoregressive, moving average, and state-space models. Students will explore foundational concepts like stationarity, seasonality, and model diagnostics, as well as advanced topics such as forecasting and intervention analysis. The course emphasizes practical applications, equipping students with the skills to apply these methods to real-world datasets using statistical software. By the end of the course, students will be prepared to analyze complex data structures involving both discrete responses and time-dependent patterns.