Identifying interpretable models from data has been an overarching goal in science. Over the past years, the Sparse Identification of Nonlinear Dynamics framework (SINDy) has sparked a renewed interest in the identification of continuous-time nonlinear dynamical systems from a limited set of available measurements. Since its introduction in 2015, SINDy has proven to be an extremely versatile framework with numerous variations proposed, e.g. vanilla SINDy, SINDy with control, MANDy, etc. In this talk, we thus aim to give the audience a general overview of the capabilities offered by the SINDy framework. For that purpose, canonical examples from dynamical system theory, Hamiltonian mechanics, chaos theory and fluid dynamics will be considered. We will most notably focus on how one can incorporate prior knowledge about the system (e.g. invariants, symmetries, etc) in the identification step. Accompanying Jupyter notebooks will also be available to guide the audience.