Doctor of Philosophy (Ph.D.)
Baylor's Ph.D. in statistical science is an ideal degree for those seeking challenging research opportunities in industrial, corporate, or academic settings. The degree requirements include a balance of statistical theory, practice experience, and increasingly important communication skills. Students must complete 75 semester hours, including a statistics core of 27 hours, a consulting-teaching practicum of 3 semester hours, 36 hours of elective courses, and 9 hours of dissertation work. Students must also demonstrate computer proficiency and pass a preliminary examination. A foreign language is not required.
In addition to course work, graduate students may participate in a variety of stipend-supported practicum experiences. Teaching opportunities range from supporting small group labs to serving as teachers-of-record in the classroom. There are also a myriad of opportunities outside the classroom including field analysis for research projects or data analysis in institutional and government settings. Students provide professional statistical expertise in Baylor University administrative offices, support ongoing human subject clinical trials, and consult with faculty and graduate students from other disciplines.
Statistics Core (27 hours)
- STA 5353 Theory of Statistics II
- STA 5380 Methods in Statistics I
- STA 5381 Methods in Statistics II
- STA 6375 Computational Statistics I
- STA 5383 Introduction to Multivariate Analysis
- STA 5362 Time Series Analysis
- STA 6382 Theory of Linear Models
- STA 6352 Bayesian Theory
- STA 6351 Large Sample Theory
Students must also take three one-hour classes of STA 5V85 Practice in Statistics, and nine hours of STA 6V99 Dissertation
Elective Courses (36 hours)
The elective courses may include any approved statistics course or approved courses in mathematics (MTH), computer science (CSI), economics (ECO), quantitative business analysis (QBA), information systems (ISY), biology (BIO), or psychology (PSY). Note that STA 5V85 does not count as an elective course.