The data-driven modeling and control of complex systems is a rapidly evolving field with great potential to transform the engineering, biological, and physical sciences. There is unprecedented availability of high-fidelity measurements from historical records, numerical simulations, and experimental data, and although data is abundant, models often remain elusive. Modern systems of interest, such as a turbulent fluid, an epidemiological system, a network of neurons, financial markets, or the climate, may be characterized as high-dimensional, nonlinear dynamical systems that exhibit rich multiscale phenomena in both space and time. However complex, many of these systems evolve on a low-dimensional attractor that may be characterized by spatiotemporal coherent structures. In this chapter, we will introduce the topic of this book, dynamic mode decomposition (DMD), which is a powerful new technique for the discovery of dynamical systems from high-dimensional data.