Applied Statistics Master of science degree
Applied Statistics
Master of science degree
In this statistics master's degree, you'll learn statistical analysis and apply it to a variety of industries, including insurance, marketing, government, health care, and more.
Overview
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.
This statistics master's degree 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 includes core courses, electives, and a capstone project or thesis.
Core Courses
Students are required to complete core courses: 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.
Electives
Elective courses are chosen by the student with the guidance of their advisor. These courses are usually department courses but may include up to 6 credit hours from other departments (or may be transferred from other universities) that are consistent with students’ professional objectives.
Capstone Thesis/Project
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
- Clinical Trails
- Data Mining/Machine Learning
- Industrial Statistics
- Informatics
National Labs Career Fair
Hosted by RIT’s Office of Career Services and Cooperative Education, the National Labs Career Fair is an annual event that brings representatives to campus from the United States’ federally funded research and development labs. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Students are invited to attend the career fair to network with lab professionals, learn about opportunities, and interview for co-ops, internships, research positions, and full-time employment.
Industries
Typical Job Titles
| Quality Engineer | Reliability Analyst |
| Quality Manager | Statistical Consultant |
| Continuous Improvement Engineer | Data Science Consultant |
Featured Profiles
Your Partners in Success: Meet Our Faculty
Dr. Tony Wong
Mathematics is a powerful tool for answering questions. From mitigating climate risks to splitting the dinner bill, Professor Wong shows students that math is more than just a prerequisite.
Curriculum for Applied Statistics MS
Applied Statistics (project option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| STAT-631 | 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. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
3 |
| STAT-641 | 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. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
3 |
| STAT-642 | Applied Linear Models - ANOVA This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, STATQL-ACT or MMSI-MS programs.) Lecture 3 (Fall, Spring). |
3 |
Electives |
9 | |
| Second Year | ||
Electives |
9 | |
| STAT-790 | Capstone Thesis/Project 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.) (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer). |
3 |
| Total Semester Credit Hours | 30 |
|
Applied Statistics (thesis option), MS degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| STAT-631 | 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. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
3 |
| STAT-641 | 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. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). |
3 |
| STAT-642 | Applied Linear Models - ANOVA This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, STATQL-ACT or MMSI-MS programs.) Lecture 3 (Fall, Spring). |
3 |
Electives |
9 | |
| Second Year | ||
Electives |
6 | |
| STAT-790 | Capstone Thesis/Project 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.) (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer). |
6 |
| Total Semester Credit Hours | 30 |
|
Admission Requirements
To be considered for admission to the MS program in applied statistics, candidates should fulfill the following requirements:
- Complete a graduate application.
- Hold a baccalaureate degree (or equivalent) from an accredited university or college.
- Have satisfactory background in mathematics (two course sequence in calculus) and one course in applied 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.
Learn about admissions, cost, and financial aid
Latest News
-
June 23, 2020
![screenshot of program that searches math formulas.]()
RIT researchers create easy-to-use math-aware search interface
Researchers at RIT have developed MathDeck, an online search interface that allows anyone to easily create, edit and lookup sophisticated math formulas on the computer. Created by an interdisciplinary team of more than a dozen faculty and students, MathDeck aims to make math notation interactive and easily shareable, and it's is free and open to the public.
-
April 12, 2018
![]()
Playful teaching style earns assistant professor two awards
Nathaniel Barlow is the winner of RIT’s Richard and Virginia Eisenhart Provost’s Award for Excellence in Teaching and the Innovative Teaching with Technology Award.
