These two lectures recap some basics of Data Science. Topics will include: high-dimensional geometry, concentration inequalities, Gaussian densities and mixtures, Singular Value Decomposition (SVD), Applications of SVD, Markov Chains, Rapid Mixing, Streaming, Randomized Algorithms for Matrices etc. For those familiar with these topics, different proofs of basic theorems (than the usual ones) may be of interest.