Laboratory automation and data science are valuable new skills for all chemists, but most pedagogical activities involving automation to date have focused on upper-level coursework. Herein, we describe a combined computational and experimental lab suitable for a first-year undergraduate general chemistry course, in which these topics are introduced in the context of determination of the solubility equilibrium constant of lead iodide. Students analyze their data using logistic regression analysis, which has a physical interpretation in terms of the solubility equilibrium expression and its stoichiometric coefficients. In addition to laboratory automation, data visualization, and data fitting skills, students also practice core laboratory skills such as the preparation of stock solutions using a volumetric flask and the use of micropipets. To keep the lab affordable, we demonstrate the use of a low-cost 3D-printed liquid dispensing robot to perform the automated experiment in addition to a commercial liquid-handling robot. Example pre- and post-lab computational notebooks are provided in both Mathematica and Python programming languages.