In this work, we examine copulas. We first define a copula as a distribution function with a uniform marginal distribution function on the interval \( [0, 1] \). We also provide their characteristics and demonstrate the simplest, independent copula. We have shown that each copula is bounded by upper and lower Fréchet-Hoeffding boundaries and that these boundaries are copulas in some dimensions. Next, we focus on Sklar's theorem, which is the central theorem of this work. We not only propose and prove it, but we also provide its interpretation. At the end of the chapter, we introduce the concept of a surviving copula and discuss the properties of radial symmetry and commutativity. The subsequent chapter delved into the concept of dependence. Our analysis comprised of the introduction of perfect dependence, independence, and tail dependence. Additionally, we scrutinized correlation coefficients and arrived at the conclusion that linear correlation coefficients are not the most effective estimators. Therefore, we introduced two alternative rank correlation coefficients: Spearman's \(\rho\) and Kendall's \(\tau\). In the third chapter, our focus was on the study of copulas and their classifications. We identified three main classes of copulas: elliptical copulas, Archimedean copulas, and extreme value copulas. Through the examination of their properties, we were able to associate different copula families with each of these classes. The second instance showcased the superiority of the \(t\)-copula over the normal copula in modeling the logarithmic returns of stocks. Lastly, we explored Felix Salmon's perspective on the financial crisis, which attributes it to the overreliance on copulas. Concluding the paper are the implementation examples, which were executed using the R programming language.