To help students understand how to analyze and process proteomics data, the final section of the course introduced the software tool Perseus. Students used a case study of a proteomics dataset produced by Geiger et al. with the Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) method. Through the module, students learned various types of proteomics data analysis using Perseus, including data filtering, normalization techniques, and intuitive representations using scatter plots, histograms, volcano plots, and intensity curves. Additionally, the module introduced Perseus-based statistical techniques such as student t-tests, principal component analysis (PCA), hierarchical clustering, network enrichment, and correlation analysis. Students were given the freedom to explore disease biology and investigate potential drug targets. This more open-ended, exploration-focused module introduced students to the initial phase of drug discovery where you find a patient need and a druggable protein/pathway that could potentially address it. This also encouraged discussion and collaboration among students allowing them to become comfortable with each other and the instructors.