This chapter introduces basic matrix operations and how to compute eigenvectors. In multivariate statistics, data is often represented as matrices, with rows and columns corresponding to observations and variables, respectively. Matrix operations are used in many multivariate statistical and machine learning methods. In addition, understanding how eigenvectors and eigenvalues are computed is important for fully understanding how PCA works—one of the most fundamental methods in multivariate statistics and machine learning. This chapter therefore provides the essential foundations you need before we begin exploring multivariate methods.