This course will introduce the principles of machine learning and data science, with a focus on applications in materials science. The fundamentals of machine learning will be emphasized along with state-of-the-art techniques. Topics include data visualization, train/test splits, cross-validation, boosting models and convolutional neural networks. Real-world materials science datasets will be used throughout, and different data formats will be considered (e.g., descriptors vs. images). Students will demonstrate their knowledge in a final project that uses data derived from actual applications.