Most generally, dynamic functional connectivity (FC) refers to the noninstantaneous couplings across time series from a set of brain areas, here as measured by fMRI. This is in contrast to static FC, which is defined as purely instantaneous relations. In this chapter, we provide a hands-on description of a nonexhaustive selection of different methods used to estimate dynamic FC (such as sliding windows, clustering approaches, Hidden Markov Models, and multivariate autoregressive models), and we explain, using practical examples, how data should be prepared for dynamic FC analyses and how models of dynamic FC can be evaluated. We also discuss current developments in the dynamic FC research field, including challenges of reliability and reproducibility, and perspectives of using dynamic FC for prediction.