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Given the diversity in educational background of our learners 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 a biologist, 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 are self-paced:

Data Analysis for Life Sciences 1: Statistics and R Data Analysis for Life Sciences 2: Introduction to Linear Models and Matrix Algebra Data Analysis for Life Sciences 3: Statistical Inference and Modeling for High-throughput Experiments Data Analysis for Life Sciences 4: High-Dimensional Data Analysis
Data Analysis for Life Sciences 5: Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays Data Analysis for Life Sciences 6: High-performance Computing for Reproducible Genomics Data Analysis for Life Sciences 7: Case Studies in Functional Genomics