One of the most commonly used methods is Principal Component Analysis (PCA). PCA seeks to find the principal directions in the data that explain most of the observed variance. To do this, we use the notion of covariance. Let's represent our data set, made up of n observations (samples) and p variables (characteristics), by a matrix X of size n x p. Before applying PCA, it is often recommended to standardize the variables so that they have zero mean and unit variance, so that variables with different scales don't dominate the analysis.