Intuitive overview of principal components analysis (PCA)

I found an excellent and short introductory tutorial pdf on principal components analysis (PCA). It provides a good overview of the following concepts in a particularly intuitive manner:

1. Mean Average
2. Standard Deviation
3. Variance
4. Co-Variance
5. Matrix transformations
6. Eigenvectors & EigenValues
7. Principal Component Analysis

Unfortunately I found the eigenvectors bit a bit heavy going. Luckily the wikipedia page for eigenvectors has a fantastic illustration on the right that gave me an instant feel of what was happening.