A matrix can hide a beautiful simplicity. The Singular Value Decomposition (SVD) peels away the layers to reveal that every matrix—no matter how messy—is just a rotation, a stretch, and another rotation. In this chapter we will see how the SVD gives us a powerful set of tools for understanding data, compressing information, and solving messy least‑squares problems.