This English-taught course is an introduction to model-based data analytics, detailing: (1) statistical programming using R and Stan, (2) stochastic simulation, (3) computationally intensive methods, (4) mixed and multilevel models, and (5) model comparison. The practical goals of the course are to teach students how to specify, code, fit, and interpret model-based inference, and appreciate the powerful things ‘model thinking’ can do for analyzing dependent data when sampling is over time, space, or within clusters. Besides demanding for mathematical and abstract reasoning, the course is ‘heavy on code’ since having ‘computational thinking’ in the digital era entails a lot of scripting and programming.