Statistical Science (M.S.)
Master of Science. Major in Statistical Science.
Students seeking admission to the MS program in Statistical Science should have completed at least two semesters in college calculus comparable to MATH 170 and MATH 175, and two classes in applied statistics including STAT 431 or a comparable course. Familiarity with programming is expected, and familiarity with numerical or statistical computing environments is desirable. Students are not required to have an undergraduate degree in statistics.
Candidates must fulfill the requirements of the College of Graduate Studies and of the Department of Statistical Science. See the College of Graduate Studies section for the applicable general requirements for M.S. degree.
An individual graduate program is tailored for the student, but all students must complete a basic core requirement of 24 credits and either:
- A thesis (STAT 5001),
- An internship report (STAT 598), or
- A consulting option or course (6 credits of STAT 597). Credits from STAT 431 will not count toward the degree.
|STAT 422||Sample Survey Methods||3|
|STAT 451||Probability Theory||3|
|STAT 452||Mathematical Statistics||3|
|STAT 507||Experimental Design||3|
|STAT 519||Multivariate Analysis||3|
|STAT 565||Computer Intensive Statistics||3|
|STAT 597||Consulting Practicum||2|
A maximum of 6 credits of STAT 500 Master's Research and Thesis may be counted toward the thesis degree option.
- The student will be able to perform common common tasks such as reading in data, performing data management, and performing routine statistical analyses using statistical software such as SAS and/or R.
- The student can apply results from probability theory and the theory of statistical inference.
- The student will be able to communicate statistical methods and information from analyses clearly and effectively both orally and in written form.
- The student will be able to apply statistical knowledge to real life problems effectively and ethically, understanding the theoretical assumptions and practical limitations of the methodologies applied.