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.