Introduction to Linear Models and Matrix Algebra
- Provider edX
- Cost Free Online Course (Audit)
- Session Self Paced
- Language English
- Certificate $149 Certificate Available
- Effort 2-4 hours a week
- Duration 4 weeks long
- Learn more about MOOCs
Taken this course? Share your experience with other students. Write review
This course is part of:
Overview
Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, 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 to perform matrix operations.
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. You will need to know some basic stats for this course. 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.
Taught by
Class Central Charts
- #1 in Subjects / Algebra & Geometry / Linear Algebra
Tags
Help Center
Most commonly asked questions about EdX
Reviews for edX's Introduction to Linear Models and Matrix Algebra Based on 11 reviews
- 5 stars 55%
- 4 stars 36%
- 3 star 0%
- 2 star 9%
- 1 star 0%
Did you take this course? Share your experience with other students.
Write a review- 1
this review helpful
Thanks
In general R programming, the mathematics and formulas are reasonably simple. But for somebody who is just starting Data Science (although I have math and programming background) I find that this course gave very little perspective on 'why' taking this approach, 'what' is the actual goal... pretty much thinking as a data scientist; I do have a big problem with not understanding the reason behind each step, although math, programming and even applying statistics formulas or matrix calculations are things I actually understand. Or is this course only for those who already have a background in Data Science?
this review helpful
this review helpful
- 1
Never Stop Learning!
