Department website: https://mathanddata.wvu.edu/
Certificate Offered
- Applied Statistics
Nature of the Program
The Department of Statistics offers a Certificate in Applied Statistics for professionals or students who want to take applied statistics courses to enhance their quantitative skills and job opportunities. Because many students receive baccalaureate degrees from colleges that do not offer undergraduate programs in statistics, and because historically statistics has been primarily a field of graduate education, a student does not need a degree in statistics to enter the certificate program. A background in mathematics, science, or engineering is reasonable preparation for graduate work in statistics.
Faculty
Associate Director
- Kenneth J. Ryan
Professor
- Kenneth J. Ryan - Ph.D. (Iowa State University)
Statistical Machine Learning, Experimental Design
Assistant Professors
- Ryan T. Hansen - Ph.D. (West Virginia University)
Combinatorics, Graph Theory - Mihyun Kim - Ph.D. (Colorado State University)
Functional Data Analysis, Functional Time Series, Extreme Value Analysis - Jason A. Palmer - Ph.D. (University of California San Diego)
Statistical Signal Processing, Machine Learning, Non-Gaussian Linear Models - Youngseok Song - Ph.D. (Colorado State University)
Statistical Network Analysis, Graphical Models, Large-Scale Inference, Robust Learning
Lecturers
- Theresa E. Boots - M.S. (West Virginia University)
Statistics - Jad Ramadan - M.S. (West Virginia University)
Statistics - Samantha K. Service - M.S. (West Virginia University)
Statistics - John N. Twist - Ph.D. (Rutgers University)
Applied Statistics, Pharmaceutical Science
Professor Emeritus
- Erdogan Gunel - Ph.D. (State University of New York, Buffalo)
Bayesian Inference, Biostatistics, Categorical Data Analysis
Associate Professors Emeriti
- Daniel M. Chilko - M.S. (Rutgers University)
Statistical Computing, Computer Graphics - Gerald R. Hobbs Jr. - Ph.D. (Kansas State University)
Biostatistics, Nonparametric Statistics, Regression Analysis
Admissions
Graduate Certificate in Applied Statistics
Students who are currently admitted to or enrolled in a graduate degree program and want to earn the Certificate in Applied Statistics (CAS) should contact the Associate Director for Statistics to enroll in the certificate program. Applicants must meet WVU’s general admission requirements. The GRE is not required for admission to this program.
All students must have successfully completed College Algebra. Single and Multi-variable Calculus are recommended.
List of Admission Requirements:
- See the steps to apply for admissions and access the application here.
- Curriculum Vitae or Resume.
- A one page (250 word) personal statement that describes the applicant’s interest in the certificate program.
International Applicants:
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See the steps to apply for admissions and access the application here.
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International applicants should view additional requirements here and here.
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English language proficiency is required in order to hold a graduate teaching assistantship. See here.
Application Deadline:
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The Certificate in Applied Statistics program admits students on a rolling basis.
For additional questions, please contact stat@mail.wvu.edu
Admission Requirements 2024-2025
The Admission Requirements above will be the same for the 2024-2025 Academic Year.
Major Code: CG29
Certificate in Applied Statistics
Certificate Code - CG29
Graduation requirement: Students must earn a minimum cumulative GPA of 3.0 and a minimum GPA of 3.0 in courses applied to the certificate.
Code | Title | Hours |
---|---|---|
CORE COURSES | 6 | |
Statistical Methods 2 | ||
Design of Experiments | ||
ELECTIVES | 9 | |
Select 9 credits from the following options: | ||
Any STAT courses at the 500 level or above | ||
Introduction to Probability Theory | ||
Theoretical Introduction to Statistical Inference | ||
Total Hours | 15 |
Certificate Learning Outcomes
Applied Statistics
Upon completion of the certificate students will be able to:
- Identify appropriate statistical methods for the analysis of real-world data;
- Analyze data using statistical programming tools;
- Apply the principles of experimental design in a science and engineering context;
- Interpret the results of designed experiments and observational studies.