Data Science

# DS 5020: Introduction to Linear Algebra and Probability for Data Science

Lecture - 4 credits

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- Offers an introductory course on the basics of statistics, probability, and linear algebra.
- Covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes' rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null-space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues, and eigenvectors.

Offers an introductory course on the basics of statistics, probability, and linear algebra.

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