DATA 5050 - Mathematics for Data Science 3

DATA 5050 Mathematics for Data Science. (3) This course covers various areas of applied mathematics relevant to data science. Topics from statistics include probability distribution functions, linear regression, and probability calculus, topics from linear algebra include vector spaces, subspaces, matrix factorization concepts, singular value decomposition, and principal component analysis, topics from discrete mathematics include combinatorics, and topics from optimization include convex optimization and constrained programming. Prerequisite(s): Prerequisite: MATH 2010 or MATH 2050 or MATH 3100 or MATH 3610 or equivalent.