This course aims to provide students with a rigorous foundation in computational statistics, combining theory with applied research practice. It covers key methods such as MCMC, EM, bootstrap, importance sampling, optimization, and simulation techniques, emphasizing both their theoretical basis and practical implementation for complex statistical challenges. The course also trains students to assess algorithm efficiency and scalability in high-dimensional or large-data contexts, and to apply these tools in an independent, project-based research setting that culminates in clear and professional scholarly communication.