In many practical situations, different types of data are generated. A data set may contain continuous, discrete, and binary data representing certain properties of objects of interest. The applications we will consider are mostly from life sciences, which implies that we will often study measurements on living entities. Understanding the relationships between variables is often considered important. In the case of medical sciences it may help us understand health and disease. This course will discuss statistical methods and models for estimation of associations and for building nonlinear relationships between variables. The theory is applicable to any area of data science.