Applied Statistics, M.S.

Applied statistics focuses on applications of statistics to the solution of functional problems in business, engineering, medicine, applied sciences, and other fields. It includes instruction in statistical techniques such as inference, probability theory, regression analysis, descriptive statistics, stochastic processes, Monte Carlo method, Bayesian statistics, non-parametric statistics, sampling theory, and statistical computing. The program builds critical thinking and real-world problems solving skills in data analysis and empirical research.

Thesis Option

30 credit hours of courses and Stat 8999 Thesis Research (6 credit hours).

Non-Thesis Option

33 credit hours of courses and Stat 8820 Research (3 credit hours).

Required STAT Courses

(4 out of 6 courses, 12 credit hours)

STAT 8090 - Applied Multivariate Statistics 3 Credit Hours
STAT 8561 - Linear Statistical Analysis I 3 Credit Hours STAT 8630 - Experimental Designs 3 Credit Hours
STAT 8670 - Computational Methods in Statistics
STAT 8678 - SAS Programming and Data Analysis
MATH 8535 - Applied Matrix Algebra 3 Credit Hours

Elective Courses

STAT 8310 - Bayesian Data Analysis 3 Credit Hours
STAT 8440 - Survival Analysis 3 Credit Hours
STAT 8540 - Advanced Methodologies in Biostatistics 3 Credit Hours
STAT 8550 - Applied Functional Data Analysis 3 Credit Hours
STAT 8581 - Statistical Theory I 3 Credit Hours
STAT 8582 - Statistical Theory II 3 Credit Hours
STAT 8600 - Probability Theory 3 Credit Hours
STAT 8610 - Time Series Analysis 3 Credit Hours
STAT 8674 - Monte Carlo Methods 3 Credit Hours
STAT 8680 - Applied Nonparametric Methods 3 Credit Hours
STAT 8700 - Categorical Data Analysis 3 Credit Hours
STAT 8760 - Sample Surveys 3 Credit Hours
STAT 8800 - Statistical Consulting I 3 Credit Hours
MATH 8600 - Introduction to Deep Learning and its Mathematical Foundations 3 Credit Hours NEW
CSC/Data Science 8000 level courses (2 courses, 6 credit hours)