INTRODUCTION TO LINEAR MODELS AND MATRIX ALGEBRA

This four-week course, offered at a self-paced pace, delves into the world of Data Science. Conducted in English with English video transcripts, it caters to intermediate learners. You have the option to audit the course for free or enhance your learning by adding a Verified Certificate for $129.

You will master essential topics in matrix algebra, including notation and various operations. These skills will be directly applicable to data analysis, where you’ll explore linear models and get a brief introduction to the QR decomposition method. By the end of this course, you’ll have a solid foundation in matrix algebra and its real-world applications in data analysis.

About this course

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 two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

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

Genomics Data Analysis:

PH525.5x: Introduction to Bioconductor
PH525.6x: Case Studies in Functional Genomics
PH525.7x: Advanced Bioconductor