Extracting information from data, Mathematics as a foundation of data science, Data Science models and methods, data science revolution, Topics from real analysis and functional analysis, matrix algebra, multivariate calculus, differential equations, geometry and topology. Data visualization, mining and knowledge discovery, geometry and high dimension, dimension reduction, high-dimensional statistics, Concentration of Measure, graphs and networks, dimension and Semi-supervised learning, Matrix concentration inequalities, Manifold Learning and Diffusion Maps, Compressive Sensing and Sparsity, tensor decomposition, learning for control and dynamical systems, physics of information, Quantum Machine Learning, Solving complex problems.