Data Analysis for Life Sciences 2: Introduction to Linear Models and Matrix Algebra


    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

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