## PCA, Eigenvectors and the Covariance Matrix

Almost every data science course will at some point (usually) sooner than later cover PCA, i.e. Principal Component Analysis. PCA is an important tool used in exploratory data analysis for dimensionality reduction. In this post I want to show you (hopefully in an intuitive way) how PCA works its mathematical magic. Let’s start with a short … Read more PCA, Eigenvectors and the Covariance Matrix