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.