Fundamentals of Data Science Part II

In Part II of this series, we cover the elements of statistical modeling, focusing on:validation methodology
principles of object-oriented design
linear and logistic regression
generalized linear models
causality
time series analysis
Bayesian statistics, including simulations in pymc3
Modeling customer lifetime values, including a detailed study of the beta-Bernoulli/beta-binomial model, a discretized version of the classic Pareto/NBD
an introduction to credibility theory
The theory is illustrated with simulations in Python throughout the text.