Fundamentals of Data Science Part II

<p><strong>In </strong><strong style=color: rgba(0 167 217 1)>Part II</strong><strong> of this series we cover the elements of statistical modeling focusing on:</strong></p><ul><li><strong>validation methodology</strong></li><li><strong>principles of object-oriented design</strong></li><li><strong>linear and logistic regression</strong></li><li><strong>generalized linear models</strong></li><li><strong>causality</strong></li><li><strong>time series analysis</strong></li><li><strong>Bayesian statistics including simulations in pymc3</strong></li><li><strong>Modeling customer lifetime values including a detailed study of the beta-Bernoulli/beta-binomial model a discretized version of the classic Pareto/NBD</strong></li><li><strong>an introduction to credibility theory</strong></li></ul><p><strong>The theory is illustrated with simulations in Python throughout the text.</strong></p><p><br></p><p><br></p>