Statistics, PhD

The Ph.D. program in Statistics is a research-focused program that will train students to develop theory, methods, and tools for the analysis of increasingly diverse emerging data types. The proposed program is unique in that it combines a traditional Ph.D. program in Statistics, covering many areas of research, with an unusually significant research strength in computational statistics, which is a very important area of training in this data-centric era. In fact, the training in computational statistics that the proposed program will deliver is an essential component of the highly-skilled workforce for which Ontario is striving.

In addition to the traditional entry route, i.e., after completion of a suitable master’s degree, students can transfer into the Ph.D. program in Statistics from the M.Sc. in Statistics or can avail of direct entry. Students in the Ph.D. program in Statistics take at least two graduate level courses and complete a two-part comprehensive examination in addition to their research work over the four years of the program. In addition to gaining subject-matter expertise in statistics, students will gain valuable communications skills – both written and oral. The fostering of independence is another crucial part of the training and is manifest in the evolution of the supervisor-student relationship; specifically, as the student progresses towards the end of the program, the regular meetings with the supervisor, while maintaining a mentor-mentee tone, becomes more like a discussion between two collaborators.

Graduates of the Ph.D. program in Statistics will have a deep understanding of relevant statistical theory as well as an ability to develop computational and statistical tools for the analysis of modern data. By producing such graduates, the Ph.D. program in Statistics will help address the acute shortage of highly qualified statisticians, especially those trained to the doctoral level, who understand theory and can develop computational and statistical tools for the analysis of diverse emerging data types.