Fundamentals of Data Science Trilogy

<p><strong>In this series we cover statistical experimentation modeling and machine learning focusing on:</strong></p><ul><li><strong>basic statistics hypothesis testing and experimentation</strong></li><li><strong>validation methodology</strong></li><li><strong>linear and logistic regression; generalized linear models</strong></li><li><strong>causality</strong></li><li><strong>time series analysis</strong></li><li><strong>Bayesian statistics</strong></li><li><strong>clustering</strong></li><li><strong>decision trees random forests and boosted forests</strong></li><li><strong>artificial neural networks and deep learning</strong></li><li><strong>reinforcement learning</strong></li></ul><p><strong>Our 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. </strong></p><p><br></p><p><br></p>