This course provides students with the essential background in calculus and linear algebra needed to pursue the study of Data Science. Topics include limits, derivatives and integrals of (multivariable) functions; vectors and matrices; vector spaces and subspaces; norms and projections; basis and dimension; eigenvalues and eigenvectors; singular values; continuous optimization; and maps between Euclidean spaces and Jacobians. Throughout, various applications to Data Science will be considered, with hands-on numerical and coding exercises supplementing the theory.