Ensemble data assimilation (EnDA) is used to combine numerical models and observations in a quantitative way. EnDA allows us to join the information from model and observations, e.g. for a better estimate of the system state in all variables represented by the model, include those which are not observed. Further, one can improve the model formulation through the estimation of model parameters. An ensemble of model state realizations is used to estimate the uncertainty of the model state and correlations between different variables. To simplify the implementation of EnDA with numerical models, the open-source Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) has been developed. PDAF provides support for the ensemble simulations and optimized filter algorithms so that one can implement the EnDA with very small changes to a model code. This tutorial will first provide an overview of possibilities and components of EnDA. Subsequently, the example of combining the ocean general circulation model MITgcm with PDAF will be used to discuss the required implementation steps for adding EnDA to a model. The tutorial should be useful for scientists to get an overview of the EnDA methodology and to learn how ensemble data assimilation can be added to a numerical model.