Sonia Baee
What are the contributing factors to the severity of train accidents? How do you predict if an e-mail is spam? How can you translate goal-directed problems such as these into actionable decisions and meaningful recommendations that can have vast societal implications? How can you harness multi-dimensional, heterogeneous data to analyze the problem? In this course, we will explore Evidence Informed Systems Engineering (EISE) practices and how they can be applied to difficult, open-ended problems. The primary
SYS 3060, SYS 3034, and APMA 3012 or equivalent. It is recommended that students have a basic command of linear algebra, calculus, and statistics. We will use R for data analysis and R Studio for our programming sessions. Student are encourage to familiarize themselves with R programming , R for Data Science , and R Studio .
No required textbook, but students are encouraged to read chapters from: Linear Models with R by Julian Faraway. An Introduction with Statistical Learning by Gareth James and et. al. Applied Linear Statistical Models by Michael H. Kutner, Christopher J. Nachtsheim, John Neter. Using R for Introductory Statistics, simpleR by John Verzani.
Sonia Baee