This course provides an introduction of mathematical data science, including high-dimensional data analysis and basic machine learning algorithms. Topics include singular value decomposition, low-rank approximation, principal component analysis, k-means, spectral clustering, topic models, nonnegative matrix factorization, together with various applications such as compressive sensing, image recovery and natural language processing.