Developed and delivered the Skore Data Accessor for Dataset Statistics and Visualization within the probabl-ai/skore repository, focusing on enhancing data exploration for both training and testing datasets. The work centered on implementing methods to plot distributions and correlations, streamlining the process of assessing data quality and supporting reproducible workflows in model development. Leveraging Python, Sklearn, and Skrub, the developer updated test requirements and provided clear example usage to facilitate adoption. The feature enabled faster, more informed data-driven decisions by making dataset statistics readily accessible, with all engineering efforts directed toward robust feature delivery and improved tooling rather than bug fixes.