Apply the skills learned in statistical analysis to a variety of industries, including insurance, marketing, government, health care, and more.
The MS in applied statistics focuses on data mining, design of experiments, health care applications, and the application of statistics to imaging and industrial environments. You’ll integrate knowledge learn through engaging courses to solve more complex problems for a wide range of organizations, including industrial, marketing, education, insurance, credit, government, and health care.
The MS program in applied statistics is available to both full- and part-time students with courses available both on-campus and online. Cooperative education is optional. The program is intended for students who do not wish to pursue a degree beyond the MS. However, a number of students have attained doctorate degrees at other universities.
Plan of study
The program requires 30 credit hours and includes four core courses, electives, and a capstone project or thesis.
Students are required to complete four core courses: Statistical Software (STAT-611), Foundations of Statistics (STAT-631), Applied Linear Models–Regression (STAT-641), and Applied Linear Models–ANOVA (STAT-642). Students, in conjunction with their advisors’ recommendations, should take the core courses early in the program.
Elective courses are chosen by the student with the help of their advisor. These courses are usually department courses but may include (or transferred from other universities) up to 6 credit hours from other departments that are consistent with students’ professional objectives.
The capstone project is designed to ensure that students can integrate the knowledge from their courses to solve more complex problems. This project is taken near the end of a student’s course of study. Students, with advisor approval, may write a thesis as their capstone. A thesis may be 3 or 6 credit hours. If a student writes a 6 credit hour thesis, they would be required to complete four elective courses instead of five.
Areas of concentration
Data Mining/Machine Learning
Government (Local, State, Federal)
Scientific and Technical Consulting
Biotech and Life Sciences
Typical Job Titles
Continuous Improvement Engineer
Data Science Consultant
Applied Statistics, MS degree, typical course sequence
Sem. Cr. Hrs.
This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.
Foundations of Statistics
This course introduces principles of probability and statistics with a strong emphasis on conceptual aspects of statistical inference. Topics include fundamentals of probability, probability distribution functions, expectation and variance, discrete and continuous distributions, sampling distributions, confidence intervals and hypothesis tests.
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.
Applied Linear Models - ANOVA
This course is a graduate course for students enrolled in the Thesis/Project track of the MS Applied Statistics Program. (Enrollment in this course requires permission from the Director of Graduate Programs for Applied Statistics.)
Hold a baccalaureate degree (or equivalent) from an accredited university or college.
Have satisfactory background in mathematics (one year of differential equations and integral calculus) and statistics (two courses in probability and statistics).
Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
Have knowledge of a programming language.
Have a minimum cumulative GPA of 3.0 (or equivalent) (recommended but not required).
GRE scores are not required. However, in cases where there may be some question regarding the capability of the applicant to complete the program. Applicants may be asked to submit scores to strengthen their application.
Submit a current resume or curriculum vitae.
Submit two letters of recommendation from academic or professional sources.
International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.