This course will equip the students with the fundamental skills and knowledge for: understanding the statistical foundation of data science and machine learning methods; approaching active and passive data as artifacts for scientific evaluation; combining, pre-processing, and cleaning data in practical data science projects; performing exploratory data analysis and uncovering patterns in data; analyzing data and making inference using methods from statistical learning; resampling data and evaluate the error of any computational estimate; using confidence intervals, analysis of variance, and hypothesis testing to explain data; implementing linear and nonlinear regression models for prediction and inference; designing and understanding tree-based models and support vector machines; detecting and avoiding misleading statistical figures, information visualization, and other forms of data presentation which lack a logical coherence. This is an intensive and high-demand course which requires active engagement and participation.