Algorithmic Foundations of Data Science

(4 Credits)

Modern computational problems frequently involve processing massive amounts of data which are often not even available in advance but arrive at a high rate. Apart from the volume and speed, the data are often very high dimensional and noisy. The goal of this course is to teach foundational algorithmic techniques that can be used to build scalable and robust solutions for practical problems of this nature. Topics include map-reduce, near-neighbor search, clustering, regression and dimensionality reduction, streaming and sketching, graph analysis, and fundamentals of machine learning. The course will be taught using the Python programming language and assumes familiarity with the language.