Prerequisites: CS 141, CS 100; STAT 155 or EE 114 or equivalent; graduate standing; or consent of instructor
Description: Covers important algorithms relevant to the lifetime of data from data collection and cleaning to integration, data mining, and analytics. Topics include: sketch algorithms for computing statistics on data streams; mining social graphs including community detection and graph partitioning; Data Science life cycle: techniques on data cleaning, data integration, Exploratory Data Analysis, and visualization.
Cross-listing: Cross-listed with CS 252A.