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DS 5020 - Introduction to Linear Algebra and Probability for Data Science |

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.
4.000 Credit hours 4.000 Lecture hours Levels: Graduate Schedule Types: Lecture Data Science Department Course Attributes: Graduate CCIS Data Sci Cert Restrictions: Must be enrolled in one of the following Programs: MS Data Science Must be enrolled in one of the following Levels: Graduate |

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