This course introduces graduate students to contemporary numerical methods and techniques for data generation and analysis. The course program includes numerical approximation theory, neural-network-based methods for solving PDEs and inverse problems, neural operators for parametric PDEs, methods for dimensional reduction, including diffusion maps and autoencoders, generative models, and graph data analysis (if time allows).