School of Engineering


Elective courses are intended to allow students to pursue topics of personal interest in engineering, computer science, statistics, business, and other fields.
  • Elective courses outside of the School of Engineering require the written approval of the graduate program director prior to the start of the class.  A form is available on the School of Engineering web site:
  • At most, nine credit hours of course work not taken in the School of Engineering may be applied toward the MSE degree. This includes transfer course work as well as electives.
  • A student may elect to take any 600-level engineering course as an elective.  Pre-requisite knowledge may be required.
  • Independent study courses may be arranged through the graduate program director.
    • These courses provide the opportunity to study closely with a faculty member on a topic not covered by the regularly scheduled courses. 
    • Independent study may be used to extend the capstone experience (EGR 692/693 or EGR 696/697) to 7, 8, or 9 credits.
    • Typical courses are in biomedical engineering and advanced production operations areas.
  • A graduate practicum experience is available for full-time students. This experience provides the opportunity to work with a local company full time for one semester for 3 credit hours. This experience should be arranged through the graduate program director.
  • Courses that are redundant with existing School of Engineering classes may not be taken for credit toward MSE program requirements.

    • MGT 561 and MGT 661 are redundant with EGR 640 and thus may not be taken for credit toward the MSE program requirements.
  • STA 610 gives students a useful overview of statistical methods.

STA 610, Applied Statistics for Health Professions:  Project-oriented overview of major statistical techniques commonly used in problems encountered in health professions. Students will learn to use a major statistical computing package. Hypothesis testing,
t-tests, regression, analysis of variance, analysis of covariance, categorical data analysis, nonparametric statistics.

Page last modified December 11, 2009