Symbolic control is a computational approach to control nonlinear systems subject to state and input constraints. It is based on the use of symbolic models that are sound discrete-state approximations of the original dynamics. Symbolic models can be used to synthesize automatically controllers for the original system while providing safety guarantees. In the first part of the talk, I will give a short introduction to the field of symbolic control. The second part of the talk will be devoted to recent developments. I will show how symbolic control and model predictive control can be combined to design controllers with safety guarantees and optimized performance. I will also present recent approaches to compute symbolic models directly from data, paving the way to the development of safe learning approaches for nonlinear systems.