This chapter introduces copula-based methods for dependence modeling. It presents empirically most useful bivariate copulas, explores their properties and dependence measures, and explains their estimation from a practical perspective. Additionally, the chapter covers model selection, goodness-of-fit testing, marginal distribution modeling, and data preparation techniques. Advanced topics are also discussed, including copula-based models for bivariate binary choice problems and extensions to higher dimensions, such as elliptical copulas, vine copulas, and factor copulas. To facilitate hands-on learning, we provide a small MATLAB toolbox for basic copula modeling, accompanied by two illustrative examples—one using simulated data and another based on real-world data. The chapter concludes with references to additional freely available software resources.