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Introduction to Linear Models and Matrix Algebra

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Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

Harvard University Online Course Highlights
  • weeks long
  • 2-4 hours per week
  • Learn for FREE, Ugpradable
  • Self-Paced
  • Taught by: Rafael Irizarry, Michael Love
  • View Course Syllabus

Online Course Details:

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.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

  • PH525.1x: Statistics and R for the Life Sciences
  • PH525.2x: Introduction to Linear Models and Matrix Algebra
  • PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
  • PH525.4x: High-Dimensional Data Analysis
  • PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays
  • PH525.6x: High-performance computing for reproducible genomics
  • PH525.7x: Case studies in functional genomics

This class was supported in part by NIH grant R25GM114818.


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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

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