Applied Statistics Advanced Certificate
An applied statistics certificate for engineers, analysts, and other professionals to develop a deeper understanding of the statistical methods related to their fields.
$84K+
Average Annual Salary for Statisticians
34%
Employment Growth for Statisticians
47%
Postings for Jobs Requiring SAS
190%
Demand Growth for Artificial Intelligence
Overview for Applied Statistics Adv. Cert.
Why Study RIT's Applied Statistics Certificate
Complete the statistics certificate in four courses.
A flexible on-campus or online graduate-level credential.
Apply the courses to RIT's MS in applied statistics.
The advanced certificate in applied statistics is designed for engineers, scientists, analysts, and other professionals who want a solid education in the statistical methods that are most closely related to their work.
Graduate Certificate in Applied Statistics: On-Campus or Online
RIT’s advanced certificate in applied statistics is a flexible, on-campus or 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—pay nearly 40% higher 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.
Applied Statistics Courses
The graduate certificate in statistics requires two core courses and two elective courses. In addition to earning this credential, you also have access career counselors in RIT’s Office of Career Services and Cooperative Education who provide advice to help you plan, prepare for, and meet your career goals.
A Foundation for the MS in Applied Statistics
With your graduate certificate in statistics, the MS in applied statistics is within reach. The four courses you will complete for the graduate certificate count toward the master's degree in applied statistics. To learn more visit the MS in applied statistics program page or complete the Graduate Information Request Form.
What is a Graduate Certificate?
A graduate certificate, also called an advanced certificate, is a selection of up to five graduate level courses in a particular area of study. It can serve as a stand-alone credential that provides expertise in a specific topic that enhances your professional knowledge base, or it can serve as the entry point to a master's degree. Some students complete an advanced certificate and apply those credit hours later toward a master's degree.
Curriculum for 2024-2025 for Applied Statistics Adv. Cert.
Current Students: See Curriculum Requirements
Applied Statistics, advanced certificate, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| 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 class is restricted to students in the APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Fall, Spring, Summer). |
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, or APPSTAT-U programs.) Lecture 3 (Spring, Summer). |
3 |
Electives |
6 | |
| Total Semester Credit Hours | 12 |
|
Electives
| Course | |
|---|---|
| ISEE-682 | 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). |
| STAT-611 | 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). |
| STAT-621 | 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). |
| STAT-670 | 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). |
| STAT-672 | 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). |
| STAT-675 | 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). |
| STAT-745 | 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). |
| STAT-747 | 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). |
| STAT-753 | 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). |
| STAT-756 | 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). |
| STAT-773 | 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). |
| STAT-775 | 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). |
| STAT-776 | Causal 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). |
| STAT-784 | 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). |
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.
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Admissions and Financial Aid
This program is available on-campus or online.
On Campus
| Offered | Admit Term(s) | Application Deadline | STEM Designated |
|---|---|---|---|
| Part-time | Fall or Spring | Rolling | No |
Online
| Offered | Admit Term(s) | Application Deadline | STEM Designated |
|---|---|---|---|
| Part-time | Fall, Spring, or Summer | Rolling | No |
Part-time study is 1‑8 semester credit hours. RIT will not issue a student visa for advanced certificates.
Application Details
To be considered for admission to the Applied Statistics Adv. Cert. program, candidates must fulfill the following requirements:
- Complete an online graduate application.
- 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. A minimum cumulative GPA of 3.0 (or equivalent) is recommended.
- Satisfy prerequisite requirements and/or complete bridge courses prior to starting program coursework.
- Submit a current resume or curriculum vitae.
- Submit a personal statement of educational objectives.
- Submit two letters of recommendation.
- Entrance exam requirements: None
- Submit English language test scores (TOEFL, IELTS, PTE Academic), if required. Details are below.
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.
| TOEFL | IELTS | PTE Academic |
|---|---|---|
| 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
Prerequisites
Applicant must have college-level credit or practical experience in mathematics and statistics (two courses in probability and statistics).
Online Degree Information
The online applied statistics advanced certificate can only be completed part-time, taking one or two courses per term. The average time to completion is one year. All courses are asynchronous and your academic advisor will work with you to select courses that meet your degree requirements and your schedule. Students typically spend 10-12 hours per week per class, depending on the content and their background knowledge. A successfully completed applied statistics advanced certificate can be “stacked,” and will award 12 credits toward our applied statistics MS. 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 advanced certificate 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.
Contact
- Lindsay Lewis
- Senior Assistant Director
- Office of Graduate and Part-Time Enrollment Services
- Enrollment Management
- 585‑475‑5532
- lslges@rit.edu
- Tamas Wiandt
- Director, Applied Statistics Graduate Programs
- School of Mathematics and Statistics
- College of Science
- 585‑475‑5767
- tiwsma@rit.edu
School of Mathematics and Statistics