Reproducibility is a cornerstone of science. However, most data science projects and notebooks struggle at the most basic level of declaring dependencies correctly. A recent study showed that 42% of the notebooks executed failed due to missing dependencies.

FawltyDeps is a dependency checker that finds imports you forgot to declare (undeclared dependencies), and packages you declared, but that are not imported in your code (unused dependencies).

This talk will guide you through integrating FawltyDeps in your manual or automated workflows and how this can improve the reproducibility of your notebooks and projects.