Abstract: One of the problems in the information age is explosion of data. As such, mathematicians, statisticians and computer scientists have developed numerous data compression methods. We will talk about one fairly rudimentary method of data compression called the low-rank approximation using Singular Value Decomposition. If time permits, we will use this method to shrink some small image files (bit maps) at the end of the talk using Mathematica. Singular Value Decomposition is what some instructors call "the pinnacle of undergraduate linear algebra," but if you are willing to take some results by faith (i.e. without proofs,) the talk should be accessible to everyone. We will quickly go over all linear algebra needed. We hope that students who have taken (or are currently in) MATH 128/129 will have the added benefit of being able to appreciate more the usefulness of their dreaded course material.