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Fall 2017 Semester
Nov 18, 2017
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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 Levels:     
      Graduate

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