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Statistical and Mathematical Methods
Enrollment is Closed

This course introduces statistical and mathematical methods needed in the practice of data science. It covers basic principles in probability, statistics, linear algebra, and optimization.

About This Course

This course introduces statistical and mathematical methods needed in the practice of data science. It covers basic principles in probability, statistics, linear algebra, and optimization.

  • Probability: Probability basics (axioms of probability, conditional probability, random variables, expectation, independence, etc.), multivariate distributions, introduction to concentration bounds, laws of large numbers, central limit theorem.
  • Statistics: Maximum a posteriori and maximum likelihood estimation, minimum mean-squared error estimation, confidence intervals.
  • Linear algebra: Vector spaces, linear transformations, singular value decomposition, eigendecomposition, principal component analysis, least squares, regression.
  • Optimization: Matrix calculus, gradient descent, coordinate descent, introduction to convex optimization.

Credits:

3 credit hours; This 15 week course requires students to spend approximately 8 hours a week on course work.

Related Programs:

Master of Science in Data Science (M.S)