Applied Statistics Advanced Certificate

A graduate credential driven by real-time employer demand

Applied Statistics jobs will grow 34% by 2026, four times faster than the overall labor market.

The applied statistics job market


Average Annual Salary for Statisticians


Employment Growth for Statisticians


Postings for Jobs Requiring SAS


Demand Growth for Artificial Intelligence

Program Highlights

RIT’s advanced certificate in applied statistics is a flexible, online graduate-level credential. Designed for working professionals from a variety of disciplines who need to add statistical analysis skills to their resume through part-time study. With this credential, you’ll gain the skills you need to apply to your job immediately, and to increase your value and marketability in today’s data-rich environment. 

Recent studies show that “hybrid jobs”—those requiring complex sets of skills from different fields—are up to 40% higher paying than their single-focus counterparts, and are on the rise in every domain of business. The advanced certificate in applied statistics is a smart investment to be able to move into hybrid, complex, higher-paying jobs.

The online curriculum, faculty, and distinguished degree are identical to the on-campus advanced certificate 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.


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.
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.


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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.

A foundation for the MS Applied Statistics

With your advanced certificate in applied statistics, the MS in Applied Statistics is within your reach. The certificate’s 12 credits/four courses will count toward your 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.


The online advanced certificate in applied statistics requires 12 credits and costs $1,191 per credit hour (Academic Year 2020 – 2021). This tuition reflects the RIT Online discount of 43% off the advanced certificate 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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