PhD in Mathematical Statistics (Research)
Study Programme Details
City: | Johannesburg |
Country: | South Africa |
Admission Sessions: | Summer Session |
Mode of Study: | Fully on Site |
About this Study Course
Embark on an intellectually enriching journey with the PhD in Mathematical Statistics (Research) at the University of South Africa (UNISA). This doctoral program is not just an educational pursuit but a gateway to an in-depth understanding and integrated knowledge of advanced applicable theory in the field of mathematical statistics. As a pure science-based research study, it challenges candidates to demonstrate high-level research capability and make a significant, original academic contribution at the frontiers of the discipline.
This degree goes beyond theoretical engagement, demanding a very high level of intellectual, theoretical, and practical specialized science knowledge. Candidates are expected to gain insight into problems related to the field of study, applying advanced experimental methods and techniques of modern research. Fundamental scientific and academic values are woven into the fabric of this program, guiding candidates in generating, processing, interpreting, and presenting research data both orally and in written form.
Overview
Embark on a transformative journey of research and discovery with our PhD in Mathematical Statistics (Research) program (Programme Code: P2015Q) at UNISA. Available in both part-time and full-time modes of facilitation, this program is set at NQF Level 10, encompassing 360 credits (SAQA: 96969). Over a span of 4 years for full-time students and 5 years for part-time scholars, you will immerse yourself in the vibrant academic community of Auckland Park Kingsway campus. Let statistics and facts elevate your understanding, guiding you toward a future where your aspirations and academic achievements converge.
Admission Requirements
For admission to a doctoral programme, applicants must have successfully completed a relevant master programme in the same or relevant field of study or discipline as determined by the relevant Faculty Board, approved by the SHDC, ratified by Senate and contained in the relevant Faculty Rules and Regulations. The extent to which applicants meet admission requirements is assessed by the relevant Head of Department, in consultation with the prospective supervisors, in accordance with the admission requirements for the particular doctoral programme determined by the Faculty Board, approved by Senate and contained in the relevant Faculty Rules and Regulations. The Head of Department, in consultation with the relevant Executive Dean, may set additional admission requirements, as approved by the relevant faculty higher degrees committee, for a particular student. Admission requirements are department specific and approved by the Executive Dean. Admission to a Doctoral programme is not automatic even if the applicant is in possession of an appropriate preceding qualification. A department may, subject to approval by the Executive Dean, require a student to successfully complete certain specified components before the Doctoral degree can be awarded. Students applying for Doctoral degree studies in general need to have obtained their previous relevant degree with an average mark of at least 65% or equivalent. In exceptional cases a student with a mark between 60% and 64% may apply to be accepted for study provided a motivation from the Supervisor and the Head of Department where the study is to be conducted is submitted to the Executive Dean of the Faculty of Science for approval.
- Advanced Statistical Modeling
- Bayesian Statistical Analysis
- Multivariate Statistical Methods
- Dissertation Research and Writing
- Advanced Topics in Mathematical Statistics
For more information, please visit respective university web page link.
Future Career Outcomes
Explore the tangible success stories of our alumni, seamlessly integrating their skills into various industries. Understand how industry demands align seamlessly with the program’s focus, ensuring that graduates possess the skills demanded by the evolving job market. Witness firsthand the relevance of skills development in shaping the future of statistical research.