SEM 527 - Engineering Data Analytics

Lecture Hours: 4
Lab Hours: 0
Credit Hours: 4

Engineering Data Analytics introduces students to the technologies and methodologies needed for data-driven decision-making during all stages of product development. Students will learn how to analyze, process, and establish correlations using data from various engineering processes during the design phase, prototyping phase, and production and operation phase. Students will examine large data-sets from smart homes, large-scale to IoT (Internet of Things) applications, and IC design and manufacturing. Correlations will be established using linear regression, Anova, and other data relationship techniques. Students will use advanced software tools such as Tibco Spotfire and R to analyze “Big Data”, establish correlations, and determine if processes are capable and in control. An introduction to machine learning and real-time streaming analysis techniques will also be discussed.

Prerequisite: Graduate standing or MATH 465