Program Overview
Applied Statistics Advanced Certificate
Overview
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.
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.
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 to 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.
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.
Curriculum
Applied Statistics, Advanced Certificate, Typical Course Sequence
- First Year
- STAT-641: Applied Linear Models - Regression (3 credits)
- STAT-642: Applied Linear Models - ANOVA (3 credits)
- Electives (6 credits)
Electives
- ISEE-682: Lean Six Sigma Fundamentals
- STAT-611: Statistical Software - R
- STAT-621: Statistical Quality Control
- STAT-670: Design of Experiments
- STAT-672: Survey Design and Analysis
- STAT-675: Data Visualization & Storytelling
- STAT-745: Predictive Analytics
- STAT-747: Principles of Statistical Data Mining
- STAT-753: Nonparametric Statistics and Bootstrapping
- STAT-756: Multivariate Analysis
- STAT-773: Times Series Analysis and Forecasting
- STAT-775: Design and Analysis of Clinical Trials
- STAT-776: Causal Inference
- STAT-784: Categorical Data Analysis
Admissions and Financial Aid
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.
English Language Test Scores
International applicants whose native language is not English must submit one of the following official English language test scores.
- TOEFL: 79
- IELTS: 6.5
- PTE Academic: 56
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.
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.
Program Outline
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.
The advanced certificate in applied statistics is available both on-campus and online to accommodate diverse schedules. The program 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. These counselors provide advice to help you plan, prepare for, and meet your career goals.
A Foundation for the MS in Applied Statistics
With your advanced certificate in applied 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.
Students are also interested in: Applied Statistics MS, Applied and Computational Mathematics MS, Lean Six Sigma Adv. Cert.
Curriculum for Applied Statistics Adv. Cert.
Applied Statistics, advanced certificate, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| STAT-641 | Applied Linear Models - Regression |
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. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring). | ||
| STAT-642 | Applied Linear Models - ANOVA |
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. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, STATQL-ACT or MMSI-MS programs.) Lecture 3 (Fall, Spring). | ||
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-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-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-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.
