Courses
A total of four classes (12 credit hours) is required, including the core course MSE 723 (3 credit hours) and three MSE and ST or MA elective courses (9 credit hours). By judicious selection of elective courses, in consultation with the MI GCP Coordinator, students can customize their GCP to focus on areas of interest to them.
Required Courses
MSE 723 - Materials Informatics
Units: 3
The course aims to introduce the emergent field of materials informatics and current approaches that employ informatics and experimental and computational data to accelerate the process of materials optimization, discovery and development. An emphasis will be placed on practical implementation of machine learning techniques to various materials science problems.
Offered in Fall Only
MSE Elective Courses
Select at least one of the following:
MSE 710 - Elements Of Crystallography and Diffraction
Units: 3
Crystal symmetry, lattices and space groups; elementary diffraction by crystalline matter; experimental methods of x-ray diffraction.
Offered in Fall Only
MSE 721 - Nanoscale Simulations and Modeling
Units: 3
The course is designed to assist engineering students in learning the fundamentals and cutting-edge nature of various simulations methods. The modeling tools range from accurate first principles quantum-based approaches to multi-scale approaches that combine atomic and continuum modeling. Previous knowledge of simulations is not required. The course is appropriate for graduate students in materials science, engineering, chemistry, physics and biomedical fields.
Offered in Fall Only
YEAR: Offered Alternate Odd Years
MSE 791 - Advanced Topics in Materials Science and Engineering
Units: 1 - 3
Special studies of advanced topics in materials science and engineering.
Offered in Fall Spring Summer
Please note: MSE 791 special topics courses, Quantitative Materials Characterization Techniques and Density Functional Theory in MSE, are approved for the Materials Informatics Graduate Certificate.
ST/MA Elective Courses
Select at least one of the following:
ST 517 - Applied Statistical Methods I
Units: 3
Course covers basic methods for summarizing and describing data, accounting for variability in data, and techniques for inference. Topics include basic exploratory data analysis, probability distributions, confidence intervals, hypothesis testing, and regression analysis. This is a calculus-based course. Statistical software is used; however, there is no lab associated with the course. Credit not given for this course and ST 511 or ST 513 or ST 515. This course does NOT count as an elective towards a degree or a minor in Statistics. Note: the course will be offered in person [Fall] and online [Fall and Summer].
Offered in Fall and Summer
ST 540 - Applied Bayesian Analysis
Units: 3
Introduction to Bayesian concepts of statistical inference; Bayesian learning; Markov chain Monte Carlo methods using existing software [SAS and OpenBUGS]; linear and hierarchical models; model selection and diagnostics.
Offered in Spring Only
ST 533 - Applied Spatial Statistics
Units: 3
Introduction to statistical models and methods for analyzing various types of spatially referenced data. The focus is on applications with real data and their analysis with statistical programs such as R and SAS. Students are required to write, modify, and run computer code in order to complete homework assignments and final projects.
Offered in Spring Only
MA 540 - Uncertainty Quantification for Physical and Biological Models
Units: 3
Introduction to uncertainty quantification for physical and biological models. Parameter selection techniques, Bayesian model calibration, propagation of uncertainties, surrogate model construction, local and global sensitivity analysis.
Offered in Fall and Spring
YEAR: Offered Alternate Even Years
The fourth course will be taken from outside of the student’s degree department. For example, an MSE student’s fourth course must be from the ST or MA list (above), whereas a ST or MA student’s fourth course must be from the MSE list (above).
Academic Performance Requirements
- A minimum of a 3.000 grade point average (GPA) on all coursework taken at NC State must be maintained.
- All courses taken for certificate credit must be letter-graded and completed with a grade of “B” or better. Credit-only courses cannot be used for certificate credit.
- All grades on courses taken towards the GCP in courses at the 500-level and above are included in the GPA. Any courses taken at the 400 level and below are not eligible for certificate credit and subsequently do not affect the graduate GPA.
- Transfer credit from other institutions is not allowed for the GCP. All coursework must be registered through NC State.
- Up to two courses of post-baccalaureate coursework taken at NC State, if not already used in another graduate program, may be transferred into the GCP. All transfer credit must carry a grade of B or better.
- All GCP requirements must be completed within four (4) calendar years, beginning with the date the student commences courses applicable to the GCP.