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Introduction to Linear Models and Matrix Algebra

Harvard University via edX

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  • Provider edX
  • Subject Algebra & Geometry
  • $ Cost Free Online Course
  • Session Self Paced
  • Language English
  • Effort 2-4 hours a week
  • Start Date
  • Duration 4 weeks long

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Overview

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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HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Taught by

Michael Love and Rafael Irizarry

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Reviews for edX's Introduction to Linear Models and Matrix Algebra
4.5 Based on 8 reviews

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  • 1
Brandt P
4.0 2 years ago
by Brandt completed this course, spending 2 hours a week on it and found the course difficulty to be easy.
(Note, I took these courses before the recent reorganization. I believe the material for the first few courses is the same, so my comments should still be valid.)

This is the second PH525 sequence course offered through HarvardX. Most of the course is dedicated to demonstrating mathematical operations for performing matrix manipulations and calculations with R. Most of what is shown can be done more easily using built-in functions in R (ex: lm()), but there is still some good information here. The descriptions of collinearity, interactions, and the demonstration of building a mul…
7 people found
this review helpful
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Vlad P
4.0 3 years ago
by Vlad completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
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this review helpful
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Alun R
5.0 3 years ago
Alun is taking this course right now.
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Dennis M
5.0 3 years ago
Dennis completed this course.
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Jinwook J
5.0 2 years ago
by Jinwook completed this course.
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Gaetano P
5.0 3 years ago
by Gaetano completed this course.
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Lace L
4.0 3 years ago
by Lace partially completed this course.
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Matteo F
4.0 3 years ago
by Matteo completed this course.
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