Applied Statistics MS
Applied Statistics: Online
Master of science degree
A degree driven by real-time employer demand
Applied statistics jobs will grow 34% by 2026, four times faster than the overall labor market. Advance your career with RIT’s online MS in Applied Statistics, ranked #8 for Best Online Statistics Master’s Degree Programs.
The applied statistics job market
84k+
Average Annual Salary for Statisticians
34%
Employment Growth for Statisticians
47%
Postings for Jobs Requiring SAS
190%
Demand Growth for Artificial Intelligence
Program Highlights
The online master of science in applied statistics at RIT is ranked #8 for Best Online Statistics Master's Degree Programs by Learn.org. Offered online for more than 30 years, this degree has a proven track record in online education and preparation for statisticians in a broad range of industries and academia.
We offer start dates in the Fall, Spring, and Summer semesters. The online curriculum, faculty, and distinguished degree are identical to the on-campus degree program. You will have access to RIT’s Career Services counselors who can provide advice to help you plan, prepare for, and meet your career goals.
Designed for working professionals studying online part-time from a variety of backgrounds, you will choose electives within one or two areas of concentration tailored to your interests and career goals. You may pick a plan of study between two tracks:
- The professional track consists of core courses in the first year, and select electives in areas of study which relate to your professional goals.
- The thesis/capstone project track also consists of electives relevant to your academic and career goals, and culminates in either a thesis or capstone project.
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.
What 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
Option 1- Professional Track
STAT-631
Foundations of Statistics
Credits 3
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.
STAT-641
Applied Linear Models - Regression
Credits 3
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.
STAT-642
Applied Linear Models - ANOVA
Credits 3
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.
Option 2- Thesis/Project Track
STAT-631
Foundations of Statistics
Credits 3
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.
STAT-641
Applied Linear Models - Regression
Credits 3
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.
STAT-642
Applied Linear Models - ANOVA
Credits 3
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.
Electives
STAT-611
Statistical Software - R
Credits 3
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.
STAT-621
Statistical Quality Control
Credits 3
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.
STAT-670
Design of Experiments
Credits 3
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.
STAT-672
Survey Design and Analysis
Credits 3
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.
ISEE-682
Lean Six Sigma Fundamentals
Credits 3
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).
STAT-745
Predictive Analytics
Credits 3
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.
STAT-747
Principles of Statistical Data Mining
Credits 3
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.
STAT-753
Nonparametric Statistics and Bootstrapping
Credits 3
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.
STAT-756
Multivariate Analysis
Credits 3
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.
STAT-762
SAS Database Programming
Credits 3
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.
STAT-773
Time Series Analysis and Forecasting
Credits 3
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.
STAT-775
Design and Analysis of Clinical Trials
Credits 3
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.
STAT-784
Categorical Data Analysis
Credits 3
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.
Earn a credential as-you-go
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.
Admission Requirements
- Complete a Graduate Application.
- Hold a baccalaureate degree (or equivalent) from an accredited university or college.
- Have satisfactory background in mathematics (a two-course sequence in calculus) and one course in applied statistics. Bridge course options available.
- Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
- Submit a current resume and personal statement.
- Submit two letters of recommendation from academic or professional sources.
- A minimum cumulative GPA of 3.0 (or equivalent) is recommended but not required.
- International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE.
Cost
The online MS in Applied Statistics requires 30 credits and costs $1,191 per credit hour (Academic Year 2020 – 2021). This tuition reflects the RIT Online discount of 43% off the MS in Applied Statistics campus-based program cost.
Keep in mind that there are many options available that may help you lower your costs including:
- Military tuition benefits
- Support from employers
- Private scholarships
- Financing
- Payment plans
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