While the statistical models and tools presented in an introductory statistics course (such as linear regression) can be used to answer a wide range of questions in life sciences, many types of data can not be analyzed using these simple approaches. During this course, we will discuss statistical models and techniques beyond classical linear modelling. Following a brief review of the basics of linear regression, we will dive into more advanced topics, such as generalized and mixed-effects linear models. We will further discuss the application of mixed-effects linear models in analyzing longitudinal data. Finally, in an attempt to move beyond linearity, we will explore extensions of linear models, such as polynomial regression, splines, local regression, and generalized additive models. Throughout the course, the emphasis will be put on concrete applications in clinical and biological data analysis using real world examples.