For example, Joss et al. presented a classroom activity for predicting the normal boiling point of organic compounds using multivariate linear regression and artificial neural networks, two typical machine learning algorithms. (21) In turn, Antonelli et al. generated a lab activity for multivariate calibration using partial least-squares regression on the programing language R. (22) These hands-on experiences are a valuable asset for incorporating computational skills via the exposition of new tools to solve relevant problems in chemistry and, by doing so, to promote collaborative work and the democratization of knowledge. (13)Several general courses consisting of one or two sessions have been implemented before, including programming, data visualization, and machine learning, among others topics. As an example, The Carpentries, (23) a project focused on teaching computing skills to researchers, has hosted a number of such courses. Another important organization is the Molecular Sciences Software Institute (MolSSI), which has organized several computational chemistry-related courses. (24