Functional genomic technologies such as microarrays, sequencing, and mass spectrometry have revolutionized the way scientists gather unbiased measurements of gene expression levels on a genome-wide scale. Whether you are conducting your own experiments or seeking to explore the vast array of publicly available data sets, it is crucial to possess the necessary skills to analyze these types of experiments effectively. This comprehensive course aims to equip you with the knowledge and expertise required to analyze functional genomics data using the versatile R/Bioconductor package known as limma. Through this course, you will learn how to perform a differential expression analysis, which is a fundamental step in understanding gene expression patterns across different experimental designs. In addition to differential expression analysis, this course will also cover essential techniques such as data pre-processing, batch effect identification and correction, result visualization, and enrichment testing. These skills are crucial for ensuring the accuracy and reliability of your analysis. By the end of this course, you will have acquired a set of general analysis strategies that can be applied to gain valuable insights from any functional genomics study. This knowledge will empower you to confidently interpret and draw meaningful conclusions from your own data or publicly available datasets, contributing to advancements in the field of functional genomics.