Fundamentals of Data Science Part II

In Part II of this series, we cover the elements of statistical modeling, focusing on: validation methodologyprinciples of object-oriented designlinear and logistic regressiongeneralized linear modelscausalitytime series analysisBayesian statistics, including simulations in pymc3Modeling customer lifetime values, including a detailed study of the beta-Bernoulli/beta-binomial model, a discretized version of the classic Pareto/NBDan introduction to credibility theory

The theory is illustrated with simulations in Python throughout the text.