By the end of this course, students will be able to explain and apply advanced computational algorithms (e.g., MCMC, EM, importance sampling, bootstrap, optimization, numerical integration) to complex statistical models; implement and evaluate statistical computing methods using appropriate programming languages (e.g., R, Python, or C++), ensuring both accuracy and efficiency; critically analyze algorithm performance in terms of convergence, stability, scalability, and suitability for high-dimensional or large-scale data problems; compare and justify methodological choices by linking algorithmic properties with theoretical foundations in statistical inference; design and conduct simulation studies to assess the robustness and reliability of computational methods in applied contexts; collaborate on interdisciplinary research projects, applying computational statistics to real-world datasets from diverse domains; and lastly, communicate statistical findings effectively.