What you'll learn

  • Matrix algebra notation

  • Matrix algebra operations

  • Application of matrix algebra to data analysis

  • Linear models

  • Brief introduction to the QR decomposition

Course description

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course, we will use the R programming language.

This class was supported in part by NIH grant R25GM114818.


Assistant Professor, Departments of Biostatistics and Genetics, UNC Gillings School of Global Public Health

You may also like


Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

8 weeks long
Registration Deadline
Available now