Generalized Additive Models (GAMs) are a versatile and valuable tool for both prediction and inference in data science. They offer greater flexibility than linear models and are more interpretable than black-box methods. GAMs allow for modeling relationships in data as nonlinear functions, making them adaptable to various types of data and data science problems. This comprehensive course will provide you with a thorough understanding of GAMs and teach you how to construct them using the popular mgcv package. You will learn how to effectively interpret, explain, and visualize your model results, as well as diagnose and address any issues that may arise. Throughout the course, you will work with diverse datasets that will demonstrate the application of GAMs in different scenarios. For instance, you will explore automobile performance data to build mixed linear and nonlinear models, analyze soil pollution data to construct geospatial models, and utilize consumer purchasing data for classification and prediction purposes. By the conclusion of this course, you will have acquired a valuable toolbox of skills and techniques for solving a wide range of data science problems.