The fourth data exploration exercise introduces students to transcriptomics (RNA sequencing) data and proteomics (whole cell protein abundance measurements). The Jupyter notebook (Figure ) utilizes "in house" scripts that are basic and easy for students to interpret. Through this data exploration module, students are able to visualize correlations between RNA and protein abundance. This data exploration exercise is introduced to the students after they receive several lectures on nucleic acids and DNA sequencing and right before they receive lectures on RNA processing, transcription and translation. The data used for this Jupyter notebook is taken from from a study that engineered E. coli to produce different biofuel molecules through genetic and metabolic engineering, which used part of the dataset in Data Exploration #3. In this Jupyter notebook, students learn how to select specific proteins and RNA molecules from the dataset and plot the time series data using heatmap graphs. Through this exercise, the students learn how to compare and correlate changes between RNA, proteins and metabolites across strains of varying genetic and molecular variation.