In recent years, analytics skills have become essential for those pursuing careers in consulting and data-driven organizations. This course develops models and algorithms for central problems in analytics from a statistical perspective and discusses their impact. Topics include likelihood inference, Bayesian analysis, linear models, generalized linear models, multilevel models, spatial models, missing data, and their relationships with stochastic operations research (OR). We explain analytics from first principles of constructing different statistical models and understanding the role of hyperparameters in these models while building them up from scratch.