# PCA Why covariance matrix? [duplicate]

At PCA why we find the Eigenvalues of the covariance matrix and not the eigenvalues of the matrix $$A\times A^T$$, where $$A$$ is the data matrix and $$A^T$$ its transpose? I saw a professor at YouTube who explained PCA but he said that the solution is the eigenvalues of $$A\times A^T$$.