Data Analysis for Life Sciences 2: Introduction to Linear Models and Matrix Algebra - Harvard University
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Learn to use R programming to apply linear models to analyze data in life sciences. With an apprenticeship you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
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About this course
Basic math
Basic stats and R programming OR PH525.1x
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Subjects
- Matrix Algebra
- Algebra
- Data analysis
- Biology
- Algebra Operations
Course programme
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.
The courses will be released sequentially each month 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.
- Matrix algebra notation
- Matrix algebra operations
- Application of matrix algebra to data analysis
- Linear models
- Brief introduction to the QR decomposition
Additional information
Data Analysis for Life Sciences 2: Introduction to Linear Models and Matrix Algebra - Harvard University