Fundamentals of Data Science Trilogy

In this series, we cover statistical experimentation, modeling, and machine learning, focusing on:

basic statistics, hypothesis testing, and experimentationvalidation methodologylinear and logistic regression; generalized linear modelscausalitytime series analysisBayesian statisticsclusteringdecision trees, random forests, and boosted forestsartificial neural networks and deep learningreinforcement learningOur focus is on mathematical derivations, algorithmic development, and programming. We use Python throughout the text to support our theory. All machine learning techniques are coded from scratch using Python.