This mini-course will focus on the central role of mathematics in Data Science and machine learning. An overview of unsupervised learning and clustering will be presented along with the importance of linear algebra in our data-informed world. We will also discuss the mathematics behind recommendation systems - the algorithms behind the next video you see, the next song playing, and the products you are suggested. Different filtering and similarity metrics will be defined, applied, and implemented on different data sets. By the end of the mini-course, you will leave with a theoretical and practical (in Python) understanding of the role of mathematics in data science and its applications in biology, social change, and more.