Imagine data so large that there is not enough memory to store the data on your computer. The series of 25 lectures from Harvard University would cover topics such as Sketching/Streaming, Dimensional Reduction when data has higher dimensions, speed up clustering algorithms, large scale machine learning, deal with regression problems, compress sensing, external memory problem (measure disk I/Os instead of the number of instructions) and other such models and algorithms for big data. It would also cover topics such as Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris' algorithm.