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This short course will be an exposition of the GAMLSS framework using throughout practical examples. In particular the following topics will be covered: An introduction to GAMLSS and its statistical modelling philosophy. An introduction to the R implementation of GAMLSS. A description of the different distributions which can be used for modelling the response variable, and their properties. This includes: i) continuous (positively or negatively skewed and with high or low kurtosis) (ii) discrete (over-dispersed or zero in ated) and iii) mixed distributions. The different additive terms for modelling the parameters of the distribution will be explored including: linear, nonparametric smoothing and random effects terms. Exposition to different modelling selection techniques and diagnostics for checking the model adequacy. Further statistical modelling examples (including centile estimation).