Prerequisites: MATH 010A, MATH 031 or EE 020B; CS 100; STAT 155 or EE 114; graduate standing; or consent of instructor
Description: Explores theoretical tools in data science and their applications in data science. Introduces and motivates statistical and computational viewpoints on data analysis. Topics include the manipulation of data as vectors, drawing inferences from data as distributions, and quantifying data uncertainty for data analysis.
Cross-listing: Cross-listed with CS 252B.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.