EdX

Introduction to Linear Models and Matrix Algebra (edX)

Offered by HarvardX, Harvard University,
Introduction to Linear Models and Matrix Algebra (edX)

Learn to use R programming to apply linear models to analyze data in life sciences. 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.

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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.

This course is part of the Data Analysis for Life Sciences XSeries.

These courses make up 2 XSeries 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: annotation and analysis of genomes and genomic assays
PH525.6x: High-performance computing for reproducible genomics
PH525.7x: Case studies in functional genomics

What you'll learn:

  • Matrix algebra notation
  • Matrix algebra operations
  • Application of matrix algebra to data analysis
  • Linear models
  • Brief introduction to the QR decomposition
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