Students pursuing a graduate certificate in Computational Statistics and Data Analytics need to complete four graduate-level courses (16 credit hours). Below is the list of the two core required courses and two elective courses.
| Course | Title | Quarter Hours |
|---|---|---|
| MAT 449 | STATISTICAL DATA MANAGEMENT | 4 |
| MAT 491 | DATA MINING | 4 |
| Select two of the following: | 8 | |
| GENERALIZED LINEAR MODELS | ||
| BAYESIAN STATISTICS | ||
| APPLIED STATISTICS II | ||
| APPLIED STATISTICS III | ||
| ADVANCED STATISTICAL COMPUTING | ||
| MULTIVARIATE STATISTICS | ||
| STOCHASTIC PROCESSES | ||
| APPLIED REGRESSION ANALYSIS | ||
| SIMULATION MODELS AND MONTE CARLO METHOD | ||
| MATHEMATICAL MODELING | ||
| OPERATIONS RESEARCH: OPTIMIZATION THEORY | ||
| APPLIED TIME SERIES AND FORECASTING | ||
In special circumstances and with approval of the graduate program director, one or two of the elective courses can be substituted with other relevant courses.
Students in this certificate program must earn a grade of C- or higher in all graduate courses and finish with a cumulative GPA or 2.0 of higher.
The requirements for admission into this certificate program are:
- Bachelor's degree from an accredited institution
- Successful completion (with a grade of C- or higher) of the following undergraduate coursework:
- A year of single-variable calculus (equivalent of MAT 150-151-152)
- A course in linear algebra (equivalent of MAT 262)
- A course in statistics (equivalent of MAT 348)
- A course in computer programming (e.g., C++, Python, Java, or R)
The admission process and review of applicants is managed by the Office of Graduate Admission via the online application and follows procedures similar to those used for existing graduate programs offered by the Department of Mathematical Sciences.