This course covers the models, methods and algorithms in computational statistics. Topics include: basics of R programming; basic statistical data analysis and visualisation tools; R implementation of non-standard estimators; simulate random variables and random experiments; estimate variance and prediction performance by data partitioning and randomization (Jackknife, Bootstrap and Cross-Validation); expectation-maximization algorithm and applications; Markov Chain Monte Carlo sampling and applications. The students will learn computation-oriented statistical methods with hands-on experience on real data examples.