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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