Quantile regressions are statistical tools that describe the impact of explanatory variables on a variable of interest. They provide a more detailed picture than classic linear regression, as they focus on the entire conditional distribution of the dependent variable, not only on its mean. They are also more suited to some kind of data such as truncated and censored dependent variable, outcomes with fat-tailed distributions, nonlinear models... This document proposes a practical introduction to these tools, with a special interest on their implementation in standard statistical software (Sas, R, Stata). We also present in details two empirical applications, to help people interpreting studies that rely on these methods. Finally, we propose for more advanced readers recent extensions in particular on endogeneity issues (instrumental variables, panel data...).