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What is the intuitive relationship between SVD and PCA

I am confused between **PCA** and **SVD**.

The *wikipedia* page for the **PCA** has this line:

*"PCA can be done by eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute."*

Does this mean that **PCA = SVD** of a data matrix?

Is there an article/tutorial that explains the difference?