‘Case Studies in Functional Genomics’ will let learners understand topics such Mapping reads, Quality assessment of Next Generation Data, Analyzing RNA-seq data, Analyzing DNA methylation data and Analyzing ChIP Seq data.
‘Introduction to Bioconductor’ covers topics including high-throughput technologies, Preprocessing and normalization, Bioconductor Genomic Ranges utilities, and Genomic annotation.
‘Advanced Bioconductor’ offers understanding of Static and interactive visualization of genomic data, reproducible analysis methods, working with multiomic experiments in cancer, and more.
‘Quantitative Methods for Biology’ provides training on basics of MATLAB, Basic biological and medical applications, and troubleshooting code, among others.
‘Machine Learning and AI with Python’ will help learners examine machine learning results, recognize data bias in machine learning, and avoid underfitting or overfitting data, among other things.
‘Data Science: Probability’ will cover topics such as important concepts in probability theory, performing a Monte Carlo simulation, importance of the Central Limit Theorem, and more.