Sensitivity analysis is used to increase our understanding of the evaluated model and ease model parameter estimation. VISCOUS (VarIance-based Sensitivity analysis using COpUlaS) is a given-data, computationally frugal variance-based global sensitivity analysis framework. Grounded in Copula theory, VISCOUS computes the Sobol sensitivity indices using a probability model that describes the relationship between model inputs (e.g., the perturbations in the model parameters) and outputs (e.g., the model responses given a parameter perturbation). In this technical note, we make three contributions to make the VISCOUS framework easier to understand and apply. First, we provide additional derivations of VISCOUS to connect the VISCOUS framework to recent developments in the data science community. We provide didactic examples with simple test functions in order to help a wider group of modelers understand the underpinnings of the VISCOUS framework. Second, we evaluate the VISCOUS framework using three types of Sobol functions and provide a cautionary note on using VISCOUS to approximate Sobol’ sensitivity indices for applications where model inputs are of similar importance. Third, we provide an open-source code of VISCOUS in Python, namely, pyVISCOUS. pyVISCOUS is model-independent and can be applied with user-provided input-output data.