Data assimilation combines observational data with a numerical model. It is commonly used in numerical weather prediction, but is't also applied also in oceanography and hydrology. The integrating observations with models in a quantitative way, data assimilation allows to estimate and improve the model states, e.g. to initialize model forecasts. Also it can estimate parameters that control processes in the model or fluxes, which can be difficult and even impossible to measure. As such data assimilation can use observations to provide information about unobservable quantities if the model represents those. The combination of model and observation requires to have error estimates of both information sources. In ensemble data assimilation the error in the model state is estimated by an ensemble of model state realizations. This ensemble not only provides estimates of uncertainties, but also of cross-correlations between different model variables or parameters. The ensemble information is then used by the assimilation method, whose most widely known is the ensemble Kalman filter. To simplify the implementation and use of ensemble data assimilation, the Parallel Data Assimilation Framework - PDAF - has been developed. PDAF is a freely-available open-source software (http://pdaf.awi.de) that provides ensemble-based data assimilation methods like the ensemble Kalman filter, but also support to perform ensemble simulations. PDAF is designed so that it can be used from small toy problems running on notebook computers up to high-dimensional Earth Systems models running on supercomputers. This short course aims at geoscientists who have a modeling application or observations and are interested in applying data assimilation, but haven't found a starting point yet. The course will first provide an introduction to the ensemble data assimilation methodology. Then, it will explain the implementation concept of PDAF and finally provide a hands-on example of building a data assimilation system based on a numerical model. This practical introduction will prepare the participants to build a data assimiliton system by combining their numerical model with PDAF, hence providing a quick starting point for apply the ensemble data assimilation.