This chapter introduces the concept of dependence modeling using copulas, a statistical tool for capturing complex dependencies between variables. It begins by discussing the limitations of traditional multivariate distributions, such as the Gaussian distribution, which often fail to capture non-Gaussian characteristics and tail behaviors. The chapter then outlines the basic steps in copula construction, including univariate modeling and copula modeling for dependence. Examples are provided to illustrate how copulas can be used to model various types of dependence structures and tail behaviors. The chapter also covers the probability integral transform, which is used to convert multivariate survival functions into copula functions, and discusses the Mardia-Takahashi-Cook-Johnson (MTCJ) copula as an example. The chapter concludes with a discussion on the invariance of copulas to monotone transformations and the construction of copulas from bivariate cumulative distribution functions (CDFs).This chapter introduces the concept of dependence modeling using copulas, a statistical tool for capturing complex dependencies between variables. It begins by discussing the limitations of traditional multivariate distributions, such as the Gaussian distribution, which often fail to capture non-Gaussian characteristics and tail behaviors. The chapter then outlines the basic steps in copula construction, including univariate modeling and copula modeling for dependence. Examples are provided to illustrate how copulas can be used to model various types of dependence structures and tail behaviors. The chapter also covers the probability integral transform, which is used to convert multivariate survival functions into copula functions, and discusses the Mardia-Takahashi-Cook-Johnson (MTCJ) copula as an example. The chapter concludes with a discussion on the invariance of copulas to monotone transformations and the construction of copulas from bivariate cumulative distribution functions (CDFs).