subject
Intro

edX: Introduction to Linear Models and Matrix Algebra

 with  Michael Love and Rafael Irizarry

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.


HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

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.

8 Student
reviews
Cost Free Online Course
Pace Self Paced
Institution Harvard University
Provider edX
Language English
Certificates $49 Certificate Available
Calendar 35 weeks long

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8 reviews for edX's Introduction to Linear Models and Matrix Algebra

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4 out of 4 people found the following review useful
2 years ago
Brandt Pence 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 Read More
(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 multivariate linear model to compare treatments and interactions in particular were better than in the dedicated statistics courses I took as a graduate student.

The programming assignments are in general much easier than the material covered in the videos, and as with the first course there is a fair amount of hand-holding throughout the assignments, which is a major difference from similar courses such as the Hopkins Data Science sequence. This is a two-week course, and since I took it self-paced, it took me only a few hours (spread over two days) to finish it. Despite my 100% score, I still feel I could learn more by repeating this course down the road, and this is probably a side-effect of the ease of the homework assignments compared to the course material. There is also a lot of instances of "here is some advanced code we're using to demonstrate how this works, but we're not going to teach you how to code this", which I found to be frustrating at times. Complete .Rmd files are available for each lecture, though, so students can go through the code independently.

Overall, four stars. Maybe not the most effective use of time if all you want to do is be able to run linear regression analyses in R, but there is a lot of good information here.
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2 years ago
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Alun Ap Rhisiart is taking this course right now.
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3 years ago
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Dennis B. Mendiola completed this course.
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Gaetano Pagani completed this course.
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2 years ago
Lace Lofranco partially completed this course.
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3 years ago
Vlad Podgurschi completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
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2 years ago
Matteo Ferrara completed this course.
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