In this work, we present the most important measures of concordance, Kendall's tau and Spearman's rho. These measures describe a special dependence of random variables called concordance. First we define both measures in the case of a random sample but we will mostly focus on concordance of continuous random variables. For a more precise study of both measures Kendall's tau and Spearman's rho, we introduce function called copula, which links multivariate joint distribution functions of random vectors with their univariate marginal distributions. It has an indispensable role in a study of measures of concordance. We will prove Sklar's theorem, which will serve as a foundation for understanding measures of concordance. Finally, we will take a look into the relationship between Kendall's tau and Spearman's rho and show the most important inequalities relating both measures.