Bayesian statistics offers powerful, flexible methods for data analysis that, because they are based on full probability models, confer several benefits to analysts including scalability, straightforward quantification of uncertainty, and improved interpretability relative to classical methods. The advent of probabilistic programming has served to abstract the complexity associated with fitting Bayesian models, making such methods more widely available. PyMC3 is software for probabilistic programming in Python that implements several modern, computationally-intensive statistical algorithms for fitting Bayesian models. PyMC3’s intuitive syntax is helpful for new users, and its reliance on the Theano library for fast computation has allowed developers to keep the code base simple, making it easy to extend and expand the software to meet analytic needs. Importantly, PyMC3 implements several next-generation Bayesian computational methods, allowing for more efficient sampling for small models and better approximations to larger models with larger associated dataset. I will demonstrate how to construct, fit and check models in PyMC, using several biomedical problems as motivation.