Covers big-data-analysis techniques that scale out with increasing number of compute nodes, e.g., for cloud computing. Focuses on approaches for problem and data partitioning that distribute work effectively while keeping total cost for computation and data transfer low. Deterministic and random algorithms from a variety of domains, including graphs, data mining, linear algebra, and information retrieval, are studied and analyzed in terms of their cost, scalability, and robustness against skew. Coursework emphasizes hands-on programming experience with modern state-of-the-art big-data-processing technology. Students who do not meet course prerequisites may seek permission of instructor.
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Please read the syllabus carefully.
Go to this page for the online modules. Please make sure you go through the material before the week it is discussed in class.
See Zoom meeting links and course calendar in Canvas.