Applied Statistics Master of Science Degree

In this master’s in applied statistics, you’ll learn statistical analysis and apply it to a variety of industries, including insurance, marketing, government, health care, and more.


Outcomes Rate of RIT Graduates from this degree


Average First-Year Salary of RIT Graduates from this degree


Employment growth rate

Expected for statisticians by 2026, four times faster than the overall labor market


Merit scholarship

Average award given to accepted students

Overview for Applied Statistics MS

Why Study Applied Statistics at RIT

  • Online or On-campus: The MS in statistics is available as an online or on-campus degree program.
  • Data Driven: Learn how to use data mining, including machine learning tools and software like SAS and R, to drive insightful decision-making.
  • Tailored to your Interest: The applied statistics MS has a flexible degree plan to tailor the degree to your interests and career goals.

The master’s 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 learned through engaging courses to solve more complex problems for a wide range of organizations, including industrial, marketing, education, insurance, credit, government, and health care.

RIT’s Statistics Master’s Degree: On-Campus or Online

RIT’s master’s in applied statistics is available to both full- and part-time students with courses offered both on-campus and online. In the applied statistics master’s you will learn:

  • How to manage, analyze, and draw inferences from big data—adapting to a diverse audience using business communication skills to effectively convey your insights 
  • How to use data mining—with tools including machine learning, software like SAS and R—to drive insightful decision-making
  • How to apply statistics to the design and analysis of experiment-based industrial studies and clinical trials

Applied Statistics curriculum: Packed with high-demand skills

  • Software and Programming: Skills in Python and R are in 20% of job postings related to statistics.
  • Data Science: Demand for skills in artificial intelligence has grown 190% in the last 2 years, and machine learning is in the top 15 skills employers want.
  • Experimental Design: Crossover, adaptive, and equivalence designs are dominating 38% of this job market.
  • Modeling Techniques: Statistical analysis skills like linear, multivariate, and logistic regression are in over ⅓ of all postings for jobs in this field.

Areas of Concentration

  • Clinical Trails
  • Data Mining/Machine Learning
  • Industrial Statistics
  • Informatics


Choose your elective courses with the guidance of an 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 your professional objectives.

Capstone Thesis/Project

Practice integrating your knowledge from courses to solve more complex problems by completing a capstone project. This project is taken near the end of your course of study.

Students, with advisor approval, may write a thesis as their capstone. A thesis maybe 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.

Earn a Credential As You Study

Earn the advanced certificate in applied statistics and advance your career, all while working toward your master of science in applied statistics. These four courses may be fully applied toward the master’s degree.

Students are also interested in: Applied and Computational Mathematics MS

This program is offered on-campus or online.

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

Cooperative Education and Internships

What makes an RIT science and math education exceptional? It’s the ability to complete science and math co-ops and gain real-world experience that sets you apart. Co-ops in the College of Science include cooperative education and internship experiences in industry and health care settings, as well as research in an academic, industry, or national lab. These are not only possible at RIT, but are passionately encouraged.

What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.

National Labs Career Events and Recruiting

The Office of Career Services and Cooperative Education offers National Labs and federally-funded Research Centers from all research areas and sponsoring agencies a variety of options to connect with and recruit students. Students connect with employer partners to gather information on their laboratories and explore co-op, internship, research, and full-time opportunities.  These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Recruiting events include our university-wide Fall Career Fair, on-campus and virtual interviews, information sessions,  1:1 networking with lab representatives, and a National Labs Resume Book available to all labs.

Featured Profiles

Curriculum Update in Process for 2024-2025 for Applied Statistics MS

Current Students: See Curriculum Requirements

Applied Statistics, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
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).
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 class is restricted to students in the APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Fall, Spring, Summer).
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, or APPSTAT-U programs.) Lecture 3 (Spring, Summer).
Second Year
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).
Total Semester Credit Hours


Program Electives

Statistical Software- R
This course is an introduction to the statistical-software package R, which is 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; graphics; matrices and arrays; simulations and app development with Shiny. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
Statistical Quality Control
A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, STATQL-ACT or MMSI-MS programs.) Lecture 3 (Fall, Spring).
Design of Experiments
How to design and analyze experiments, with an emphasis on applications in engineering and the physical sciences. Topics include the role of statistics in scientific experimentation; general principles of design, including randomization, replication, and blocking; replicated and unreplicated two-level factorial designs; two-level fractional-factorial designs; response surface designs. Lecture 3 (Fall, Spring).
Survey Design and Analysis
This course is an introduction to sample survey design with emphasis on practical aspects of survey methodology. Topics include: survey planning, sample design and selection, survey instrument design, data collection methods, and analysis and reporting. Application areas discussed will include program evaluation, opinion polling, customer satisfaction, product and service design, and evaluating marketing effectiveness. Data collection methods to be discussed will include face-to-face, mail, Internet and telephone. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Summer).
Data Visualization & Storytelling
This course introduces concepts of data visualization and storytelling. Students explore the use of graphical representations of data to convey information. Topics include data visualization principles, defining a research question or business case, establishing data requirements, using R programming language to create custom plots, enhancing data visualizations and dashboards, and telling a data-driven story with visualizations. (This class is restricted to students in APPSTAT-MS.) Lecture 3 (Spring).
Lean Six Sigma Fundamentals
This course presents the philosophy and methods that enable participants to develop quality strategies and drive process improvements. The fundamental elements of Lean Six Sigma are covered along with many problem solving and statistical tools that are valuable in driving process improvements in a broad range of business environments and industries. Successful completion of this course is accompanied by “yellow belt” certification and provides a solid foundation for those who also wish to pursue a “green belt.” (Green belt certification requires completion of an approved project which is beyond the scope of this course). (This course is restricted to degree-seeking graduate students and dual degree BS/MS or BS/ME students in KGCOE.) Lecture 3 (Fall, Spring, Summer).
Predictive Analytics
This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest. (Prerequisite: This class is restricted to students in APPSTAT-MS and SMPPI-ACT who have successfully completed STAT 611 and STAT-741 or equivalent courses.) Lecture 3 (Spring).
Principles of Statistical Data Mining
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611, STAT-731 and STAT-741 or equivalent courses.) Lecture 3 (Fall, Spring).
Nonparametric Statistics and Bootstrapping
The emphasis of this course is how to make valid statistical inference in situations when the typical parametric assumptions no longer hold, with an emphasis on applications. This includes certain analyses based on rank and/or ordinal data and resampling (bootstrapping) techniques. The course provides a review of hypothesis testing and confidence-interval construction. Topics based on ranks or ordinal data include: sign and Wilcoxon signed-rank tests, Mann-Whitney and Friedman tests, runs tests, chi-square tests, rank correlation, rank order tests, Kolmogorov-Smirnov statistics. Topics based on bootstrapping include: estimating bias and variability, confidence interval methods and tests of hypothesis. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Summer).
Multivariate Analysis
Multivariate data are characterized by multiple responses. This course concentrates on the mathematical and statistical theory that underlies the analysis of multivariate data. Some important applied methods are covered. Topics include matrix algebra, the multivariate normal model, multivariate t-tests, repeated measures, MANOVA principal components, factor analysis, clustering, and discriminant analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611 or equivalent course.) Lecture 3 (Fall, Spring).
Times Series Analysis and Forecasting
This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring).
Design and Analysis of Clinical Trials
This is a graduate level survey course that stresses the concepts of statistical design and analysis for clinical trials. Topics include the design, implementation, and analysis of trials, including treatment allocation and randomization, factorial designs, cross-over designs, sample size and power, reporting and publishing, etc. SAS for Windows statistical software will be used throughout the course for data analysis. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
Casual Inference
As the need for causal discovery increases, and supportive data are increasingly available, there is a growing need to understand causal inference methods and applications beyond experiments. This course is a survey of a broad array of topics including the concepts of causal inference, causal inference methods, and applications of and implementation of causal inference techniques. Topics will include causal diagrams, and causal inference methods such as propensity score methods, instrumental variables, and methods for time-varying exposures Implementation of the methods using statistical software will be addressed. Prerequisites include a regression course and a statistical software course. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611 and STAT-641 or equivalent courses.) Lecture 3 (Spring).
Categorical Data Analysis
The course develops statistical methods for modeling and analysis of data for which the response variable is categorical. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-741 or equivalent course.) Lecture 3 (Fall, Spring).
Advanced Statistical Computing
This project-based course introduces students to advanced concepts of statistical computing. We will work in the environment of R—one of the most common and powerful statistical computing languages that are used in professional practice. Topics include: object-oriented features of R, function writing, using environments, non-local assignments (closures), and connections; converting text to code, speeding up processing, advanced features in regular expressions, introduction to the Grammar of Graphics (ggplot2) and lattice methods for graphics, R markdown, computing on large datasets (without reading all data into RAM memory), cleaning and reshaping of messy data, web scraping, interactive web applications (with Shiny), advanced reading from files and writing to files, simulations, select statistical applications. (Prerequisite: This class is restricted to students in APPSTAT-MS and SMPPI-ACT who have successfully completed STAT 611 and STAT-741 or equivalent courses.) Lecture 3 (Summer).
SAS Database Programming
This course focuses on the SAS programming language to read data, create and manipulate SAS data sets, using Structured Query Language (SQL), creating SAS macros, and SAS programming efficiency. This course covers the material required for "SAS Base Programming" and "SAS Advanced Programming " certification exams. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611 or equivalent course.) Lecture 3 (Fall, Spring).

Note for online students

The frequency of required and elective course offerings in the online program will vary, semester by semester, and will not always match the information presented here. Online students are advised to seek guidance from the listed program contact when developing their individual program course schedule.

Admissions and Financial Aid

This program is available on-campus or online.

On Campus

Offered Admit Term(s) Application Deadline STEM Designated
Full-time Fall, Spring, or Summer Rolling Yes
Part-time Fall, Spring, or Summer Rolling No


Offered Admit Term(s) Application Deadline STEM Designated
Part-time Fall, Spring, or Summer Rolling No

Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Applied Statistics MS program, candidates must fulfill the following requirements:

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

79 6.5 56

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

Cost and Financial Aid

An RIT graduate degree is an investment with lifelong returns. Graduate tuition varies by degree, the number of credits taken per semester, and delivery method. View the general cost of attendance or estimate the cost of your graduate degree.

A combination of sources can help fund your graduate degree. Learn how to fund your degree

Additional Information


  • Applicant must have college-level credit or practical experience in mathematics (two-course sequence in calculus) and one course in applied statistics.
  • Applicant must have college-level credit or practical experience in a programming language.

Online Degree Information

The online Applied Statistics MS program can only be completed part-time, taking one or two courses per term. The average time to completion is two and a half to three years. Courses in the online program can all be completed asynchronously. They are designed to accommodate working professionals and students in various time zones to provide the greatest amount of flexibility. Program electives are slightly more limited than courses available in the on-campus program. Students typically spend 10-12 hours per week per class, depending on the content and their background knowledge. The online program does not have any in-person requirements. Your academic advisor will work with you to select courses that meet your degree requirements and your schedule. Both the online and on-campus program culminates with a final capstone project. For specific details about the delivery format and learning experience, contact the Program Contact listed on this page. RIT does not offer student visas for online study.

Online Tuition Eligibility
The online Applied Statistics MS is a designated online degree program that is billed at a 43% discount from our on-campus rate. View the current online tuition rate.

Online Study Restrictions for Some International Students

Certain countries are subject to comprehensive embargoes under US Export Controls, which prohibit virtually ALL exports, imports, and other transactions without a license or other US Government authorization. Learners from the Crimea region of the Ukraine, Cuba, Iran, North Korea, and Syria may not register for RIT online courses. Nor may individuals on the United States Treasury Department’s list of Specially Designated Nationals or the United States Commerce Department’s table of Deny Orders. By registering for RIT online courses, you represent and warrant that you are not located in, under the control of, or a national or resident of any such country or on any such list.


The College of Science consistently receives research grant awards from organizations that include the National Science Foundation, National Institutes of Health, and NASA, which provide you with unique opportunities to conduct cutting-edge research with our faculty members.

Faculty in the School of Mathematics and Statistics conducts research on a broad variety of topics including:

  • applied inverse problems and optimization
  • applied statistics and data analytics
  • biomedical mathematics
  • discrete mathematics
  • dynamical systems and fluid dynamics
  • geometry, relativity, and gravitation
  • mathematics of earth and environment systems
  • multi-messenger and multi-wavelength astrophysics

Learn more by exploring the school’s mathematics research areas.

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    College of Science 2020-2021 Distinguished Alumnus: Rob Hochstetler

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  • June 23, 2020

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    RIT researchers create easy-to-use math-aware search interface

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