To enter the EMGT program, candidates must hold a bachelor’s in engineering or science. The graduate admission committee must approve the candidates to ensure they have the appropriate industrial or managerial knowledge base. Students applying for admission must have taken the GRE. Candidates must submit a GRE score of 1000 (verbal + quantitative), with a verbal score of 400 or higher. The degree plan’s admissions committee will be evaluate GRE scores. The scores are one indicator of the applicant’s potential for completing the degree plan. Candidates who apply for admission to a graduate plan should have a GPA of 3.0 or greater (on a four-point grade scale) for the last 60 hours of coursework.

The graduate degree in EMGT requires 30 hours of graduate courses. Upper-lever 4000 credits cannot apply to the EMGT master's degree. A maximum of six hours of grades which merited a “C” may count toward the graduate degree; grades of “C-“ or lower are unacceptable.

In addition, the EMGT graduate admission committee may require that applicants have a set of foundation courses and that their prerequisites be completed before they enroll in the graduate EMGT program. The foundation courses are listed below. The committee may also require industry-related experience and letters of recommendations from current employers during admission review. The EMGT faculty graduate admissions committee will approve a candidate’s acceptance into the program based on program need, program guidelines, and UHCL admission requirements. Once admitted, the candidate must file a CPS in the first semester of enrollment. The following foundation courses are required for entry:

MATH 4131  Ordinary Differential Equations and Applications

Prerequisite: MATH 3231. Solutions of ordinary differential equations of first and second order, Laplace transforms, power series techniques, systems of equations, stability, numerical methods, geometric and physical applications.

MATH 3334   Probability and Statistics for Engineers and Scientists

Prerequisite: Calculus I and II. graphical representation of data, measures of centrality and variability, concepts and rules of probability, discrete probability distribution, normal distribution, sampling distributions, central limit theorem, parameter estimation, testing of hypothesis, two sample methods, analysis of variance, correlation and regression analysis.