Linear transformations are helpful to understand using eigenvectors. They find their prime usage in the creation of covariance and correlation matrices in data science.
Simply put, eigenvectors are directional entities along which linear transformation features like compression, flip etc. can be applied.
Eigenvalues are the magnitude of the linear transformation features along each direction of an Eigenvector.