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 applied statistics MS is available as an online or on-campus degree program.
- Learn how to use data mining, including machine learning tools and software like SAS and R, to drive insightful decision-making.
- Designed for students from varied professional and academic backgrounds. Your degree begins with a plan of study tailored to your interests and career goals.
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.
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.
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Careers and Experiential Learning
Typical Job Titles
| Sr. Business Intelligence Analyst | Epidemiology Research Analyst |
| Financial Analyst | Statistician |
| Market Research Analyst | Statistical Engineer |
| Loss Forecasting and Analytics | Crime Technology Analyst |
| Advanced Quality Engineer | Principal Six Sigma Engineer |
Salary and Career Information for Applied Statistics MS
Cooperative Education and Internships
What makes an RIT education exceptional? It’s the ability to complete with real, relevant career experience that sets you apart. Experiential learning in the College of Science includes cooperative education and internships, international experiences, research, and more. Participating in these opportunities is not only possible at RIT, but passionately encouraged.
Students in the applied statistics MS are strongly encouraged to participate in cooperative education, internships, and research.
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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 an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
- Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
- Hold a baccalaureate degree (or US equivalent) from an accredited university or college.
- Recommended minimum cumulative GPA of 3.0 (or equivalent).
- Submit a current resume or curriculum vitae.
- Two letters of recommendation are required. Refer to Application Instructions and Requirements for additional information.
- Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
- Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
- Have college level credit or practical experience in mathematics (two course sequence in calculus) and one course in applied statistics.
- Have college level credit or practical experience in programming language.
- International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English requirements. International applicants may be considered for an English test requirement waiver. Refer to Additional Requirements for International Applicants to review waiver eligibility.
Learn about admissions, cost, and financial aid
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