Prerequisites: CS 252A or EE 251A or CS 235 or CS 224 or STAT 207 or STAT 208; graduate standing
Description: Covers ethics specifically related to data science. Includes data privacy; data curation and storage; discrimination and bias arising in the machine learning process; statistical topics such as generalization, causality, curse of dimensionality, and sampling bias; data communication; and strategies for conceptualizing, measuring, and mitigating problems in data-driven decision-making.
Cross-listing: Cross-listed with STAT 212.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor. Credit is awarded for one of the following CS 212, STAT 212, CS 108, or STAT 108.