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MATH-51000 Mathematics for Data Scientists


Study of mathematical concepts used in data science applications. Topics include differentiation and integration of functions, optimization techniques, matrix operations, eigenvalues and eigenvectors, curve fitting, and discrete mathematics.

Learning Objectives:

  1. Solve practical discrete mathematics and calculus problems common in statistical learning theory.
  2. Express linear models and related concepts in matrix algebra.
  3. Explore functions that model non-linearities in the data.
  4. Understand Bayesian theory used in common statistical learning applications.
  5. Explore optimization methods and understand how common iterative algorithms work.

YOUR OPPORTUNITY: You'll be prepared to comprehend and develop tools and techniques for understanding an organization's data.

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