EMSE 6035:
Marketing Analytics for Design Decisions
(Fall 2023)

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; topics include consumer choice modeling, programming in the R programming language, survey design, conjoint analysis, optimization, market simulation, and professional communication skills. 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. At the end of the semester, students will submit a final, reproducible report of their project along with a presentation of their findings. 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 weekly assignments that involve watching and reviewing recorded lecture materials and answering related practice questions.

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
  • Multivariable calculus
  • Linear algebra
  • Regression

Each of these concepts will be applied throughout the course, and no time will be spent reviewing the foundational elements of each concept.

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 three weeks going through exercises to get up to speed in R.

Students are encouraged to complete this self assessment prior to registering for the course to self-assess familiarity with these concepts.