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Functional genomics aims to identify the function of genes or proteins, and eventually the entire genome. One of the most widely used technology in the area of functional genomics is microarray technology, which is popularly used for gene expression research. Microarrays identify the genes involved in a given process by measuring the amount of mRNA in a sample, because this is indicative of the expression of genes. However, biological data suffers from a lot of noise and necessitates quality control. In this chapter, we will discuss a typical workflow involved in differential gene expression analysis using Python - starting from quality control, normalization, cluster map for differential gene expression analysis, dimensionality reduction, SNP analysis, as well as the biological interpretation of the results.