Essential Linear Algebra for Data Science

This course is part of Expressway to Data Science: Essential Math Specialization

Instructor: James Bird

What you'll learn

  •   Solve real-world problems using the foundational concept of matrices and explain where those problems might arise.
  •   Recognize what a matrix represents in n-dimensional space and how transformations act in that space
  •   Identify key properties of any system of equations, such as independence, basis, rank, and more, and what they mean for the overall system.
  •   Demonstrate your understanding of projections in lower dimensions, while being able to carry out higher dimension projections for real-world problems

    • Skills you'll gain

  •   Data Analysis
  •   Applied Mathematics
  •   Algebra
  •   Data Science
  •   Statistical Methods
  •   Linear Algebra

    • There are 5 modules in this course

    This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of Dan-Cristian Pădureț on Unsplash.com

    Matrix Algebra

    Properties of a Linear System

    Determinant and Eigens

    Projections and Least Squares

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