Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Although upstream processing of MS-derived data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various platforms, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remain challenging due to the lack of integrated tools to facilitate the analyses. To facilitate downstream interrogation of data-independent acquisition (DIA) and parallel reaction monitoring (PRM) datasets, we developed a series of Python-based Google Colab notebooks called QuickProt. These pipelines were designed so that users with no coding expertise can utilize the tools. Alternatively, as open-source code, users can customize the QuickProt notebooks and incorporate them into their workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets of a time course of erythropoiesis. The analysis outputs in annotated tables and publication-ready figures revealed a dynamic rearrangement of the proteome during erythroid differentiation, with the abundances of proteins linked to gene regulatory, metabolic, and chromatin remodeling pathways increasing early in the process. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.