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
Average award given to accepted students
Overview for Applied Statistics MS
The MS in statistics 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 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
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
Data Mining/Machine Learning
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.
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.
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.
The Power of Being Data Literate in a Data-Driven World
Melissa Royo ‘09/’10 (applied statistics)
The applied nature of the statistics programs at RIT helped Melissa Royo ’09/’10 get a sense for how real-world data behaves.
Applied Statistics, MS degree, typical course sequence
Sem. Cr. Hrs.
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).
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
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).
Survey Design and Analysis
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).
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 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).
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
Fall, Spring, or Summer
Fall, Spring, or Summer
Fall, Spring, or Summer
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.
To be considered for admission to the Applied Statistics MS program, candidates must fulfill the following requirements:
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.
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.
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.
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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.