Introduction to the mathematical foundations of data science, including calculus, linear algebra and probability. The first part of the course covers linear algebra, including matrices, systems of linear equations, vector spaces, and eigenvalues and eigenvectors. The second part of the course introduces random variables and provides an introduction to calculus based probability. The third part of the course introduces optimization techniques used in data science. Prerequisite: MATH1101 or MATH1103 or equivalent Calculus II background.
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