Department: Engineering Management and Systems Engineering @ GWU

Credits: 3


This course provides students with data analysis techniques to inform design decisions in an uncertain, competitive market. Over the course of the semester, students will learn and apply theory and methods to a team project to assess the market competitiveness of an emerging product / technology and use marketing analytics to generate design insights. This course has a “flipped” classroom structure. Students will spend the majority of class time working through guiding practice exercises or working on their projects. To prepare for class, students must complete “Pre-Class Assignments” (PCAs), which involve watching and reviewing recorded lecture materials and answering related practice questions. At the start of each class, we will review the PCA and go through the solutions to the practice questions. Students will complete a self-assessment of their answers. Working through the PCAs on schedule and participating in the self-assessments will be crucial to success in the course.

Learning Objectives:

Having successfully completed this course, students will be able to:

  • Import, wrangle, visualize, and export data in R. − Design surveys to obtain informative data about consumer preferences for product features. − Build and estimate discrete choice models. − Analyze consumer choice data to estimate consumer preferences for product features. − Design and create effective charts and presentations. − Communicate results in terms of design insights.


This course requires prior exposure to probability theory, multi-variable calculus, linear algebra, and regression. Each of these concepts will be applied throughout the course, and no time will be spent reviewing the foundational elements of each concept. To self-assess familiarity with these concepts, students can complete Assignment 0 prior to registering for the course. In addition, we will work in the R programming language throughout the course, but no prior programming experience is required. We will spend the first two weeks going through exercises to get up to speed in R.

EMSE 4197 (CRN 78916): Exploratory Data Analysis - Spring 2020
George Washington University | School of Engineering & Applied Science
Dr. John Paul Helveston | | Wednesdays | 12:45–3:15 PM | District House B205 | |
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