This course is the first course in quantitative business science for data analysts, detailing: (1) statistical programming, (2) stochastic simulation, (3) computationally intensive methods, (4) mixed and multilevel models, and (5) formal model comparison using information criteria. 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, which are common in digital operations (e.g., internet of things), platform business (e.g., Airbnb, Alibaba, Uber), and sharing economy (e.g., crowdfunding). The course is ‘heavy on code’ since having ‘computational thinking’ in the digital era entails a lot of scripting and programming.