The Master of Applied Statistics, Plan C, Statistical Science specialization emphasizes practical methods in statistics, focusing on applications and computational aspects rather than theory. The goal of this degree is to enable students to start working as practicing statisticians in industry or government immediately after graduation. Students will receive a strong background in statistical computing while completing this degree. Full time students complete the M.A.S. degree in less than a year; however, this degree may also be completed part time, either online or on campus. Students who succeed in the field of statistics typically have strong quantitative skills, analytical minds, and like to help other people solve problems.
Effective Fall 2018
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| STAA 551 | Regression Models and Applications | 2 |
| STAA 552 | Generalized Regression Models | 2 |
| STAA 553 | Experimental Design | 2 |
| STAA 554 | Mixed Models | 2 |
| STAA 556 | Statistical Consulting | 2 |
| STAA 561 | Probability with Applications | 2 |
| STAA 562 | Mathematical Statistics with Applications | 2 |
| STAA 565 | Quantitative Reasoning | 1 |
| STAA 566 | Data Visualization Methods | 1 |
| STAA 567 | Computational and Simulation Methods | 1 |
| STAA 568 | Topics Industrial/Organizational Statistics | 1 |
| STAA 574 | Methods in Multivariate Analysis | 2 |
| STAA 575 | Applied Bayesian Statistics | 2 |
| STAT 586 | Practicum in Consulting Techniques | 1 |
| Select 8 credits from the following: | 8 | |
| Survey Statistics | ||
| Nonparametric Methods | ||
| Analysis of Time Series | ||
| Methods in Spatial Statistics | ||
| Statistical Learning and Data Mining | ||
| Machine Learning | ||
| Program Total Credits: | 31 | |
A minimum of 31 credits are required to complete this program.