Introduction to Linear Models and Matrix Algebra

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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.

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 teachi...

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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Reviews 9/10 stars
3 Reviews for Introduction to Linear Models and Matrix Algebra

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klinton LINO AQUINO profile image
klinton LINO AQUINO profile image

klinton LINO AQUINO

10/10 starsTaking Now
1 year, 5 months ago
I think it's very excellent for me it would be a great opportunity to get to study a lot I like to study and also to work I am Peruvian and I want to have opportunities in my life
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Chris Snow profile image
Chris Snow profile image

Chris Snow

10/10 starsTaking Now
2 years, 4 months ago
This course has unlocked a lot of my machine learning books that I previously found inaccessible because I didn't have any experience with matrix notation. Massive thank you to the instructors and Harvard for making this course freely available.
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Marcelo Schmidt profile image
Marcelo Schmidt profile image

Marcelo Schmidt

10/10 starsTaking Now
2 years, 4 months ago
Great course motivated and developing statisticians. A few questions can be challenging if you are not familiar with R. Certificate of completion is only 50.00.
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