Graduate Certificate in Applied Statistics
Certificate Code - CG29
The Certificate in Applied Statistics (CAS) is designed for professionals or students who what to take applied statistics courses to enhance their quantitative skills and job opportunities. The certificate will provide students with a solid foundation in statistical methodology, and depending on the elective courses selected, predictive analytics, statistical computing, or statistical theory. The flexibility in the certificate course work is intended to allow the student to select courses that will meet their needs, whether enhancing professional quantitative skills or research productivity.
Admissions to the CAS may be done at any time. Students who are currently admitted to or enrolled in a graduate degree program that are wishing to earn the CAS should contact the Statistics Department to enroll in the CAS. Students who wish to pursue the CAS independent of a graduate degree program must be admitted as a non-degree graduate student prior to registering their intent to earn the certificate.
Students must earn a grade of C- in all courses applied to the CAS, and must earn at least an overall 3.0 GPA in the courses counted toward the certificate.
Students in the certificate program will complete a minimum of 15 credit hours of graduate level Statistics courses. The courses required for the completion of the CAS are defined below.
|Students must earn a grade of C- in all courses applied to the CAS.|
|Students must earn at least an overall 3.0 GPA in the courses counted toward the certificate.|
|STAT 512||Statistical Methods 2||3|
|STAT 513||Design of Experiments||3|
|Electives (500, 600, 700-level STAT Courses) *||9|
Credit towards the Certificate is also given for STAT 461 and STAT 462.
All courses applied to the certificate must be Statistics (STAT) courses; courses listed as equivalent to Statistics courses in the Catalog may not be counted.
Certificate Learning Outcomes
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.