Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Also explores scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. Includes a final project and programming assignments in which we explore the performance of our techniques when applied to publicly available data sets. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation.

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.