This course introduces fundamental principles and techniques of statistical modeling for uncovering patterns and making predictions from data. Various statistical learning methods routinely used in the fields of data science and machine learning will be covered, including linear regression, classification, random forest, support vector machines, dimension reduction, clustering, graphical models, and neural networks. The course contents include both theoretical concepts and Python coding demonstrations to help students develop a solid understanding of the core principles and gain practical experience in algorithm implementation. Through conceptual and hands-on explorations, students will acquire essential knowledge and skills for careers in a society that is increasingly driven by data and machine intelligence.