Master of Science in Mathematical Statistics

The qualification aims to educate and train researchers who can contribute to the development of knowledge at an advanced level in the field of mathematical statistics. The purpose of this qualification is to equip learners with advanced and integrated specialist knowledge and practical understanding of mathematical statistics. These learners function as effective and critical researchers who can identify, investigate and answer relevant research questions and generate new knowledge in the chosen field of study. They thus contribute towards the improved understanding of Mathematical Statistics. Successful completion of this qualification also provides candidates with the possibility of further study at the doctoral level.

Rationale

The qualification responds to the need to provide qualified learners with advanced knowledge, specific skills and applied competence in the field of Mathematical Statistics. The qualified learner with expert knowledge in statistics may enter the labour market and is equipped to make rewarding contributions to society through research activities. The qualification produces experts in the field of Mathematical Statistics to ensure that the national and international leadership base of innovative and knowledge-based scholarly activity grows. This growth includes the requirement of high-level theoretical engagement, intellectual independence and research. Successful learners can undertake all aspects of research, both individually and as part of a team, and may be able to continue with further post qualified learner studies. Qualifying learners who complete this qualification are employable nationally/internationally in different organisations, as teachers, lecturers at higher education institutions, and as researchers. Learners are ready to commence with doctoral studies.

Recognition of Prior Learning

Competence at National Qualification Framework (NQF) level 8 is assumed, and learners are expected to have a firm grounding in natural sciences with a special focus in Mathematical Statistics. Where applicants do not meet the minimum admission requirements, RPL may be used to grant access to the qualification. RPL happens according to the Recognition of prior learning, credit accumulation and transfer, and assessment (Council for Higher Education 2016) and the institution RPL policy.

Through its RPL policy and the Faculty of Natural and Agricultural Sciences ensures that quality assurance processes that address the specificities of the RPL process (including applications, assessment, and reporting and management systems). The faculty manages implementation; administration and support systems, both before and after RPL assessment. Assessment of applicants is against NQF level 8 competencies.

Entry Requirements

The minimum entry requirement for this qualification is

• Relevant Bachelor Science Honours, NQF Level 8.

This qualification comprises of compulsory modules at NQF Level 9 totalling 180 Credits.

Compulsory Modules,108 Credits

• Dissertation, 100 Credits.
• Research Methodology, 8 Credits.

Elective Modules: 72 Credits. (Choose any two).

• Advanced resampling methods, 36 Credits.
• Advanced statistical models, 36 Credits.
• Advanced Multivariate Statistics, 36 Credits.
• Advanced Probability Theory, 36 Credits.
• Advanced Time Series Models, 36 Credits.
• Advanced Stochastic Processes, 36 Credits.
• Advanced Survival Models, 36 Credits.

1. Demonstrate specialist knowledge by engaging in current research and practices within the field of Mathematical Statistics. Critically evaluate these and to contribute to disciplined thinking about relevant matters related to their specific project.
2. Apply and develop intellectual independence and advanced research skills, sophisticated knowledge and research methodologies to the solution of complex, unfamiliar problems in the field of Mathematical Statistics.
3. Design, select and apply appropriate methods, techniques, procedures or technologies to complex practical and theoretical problems within Mathematical Statistics, with specific reference to their specialisation area.
4. Analyse complex research questions in the field of Mathematical Statistics and to apply specialised problem-solving skills in identifying, conceptualising, designing and implementing methods of inquiry to solve problems within their specialisation area.
5. Design and implement a strategy for the processing and management of information, to conduct a comprehensive review of leading and current research in an area of specialisation to produce significant insights.
6. Engage and initiate in academic and scientific discourse to report and defend substantial ideas that are the results of research in Mathematical Statistics.
7. Make recommendations regarding the findings of their research and how this relates to or can influence future research in the field of Mathematical Statistics.
8. Apply high levels of responsibility, self-reflexivity and adaptability in own management of learning and analyse and evaluate ethical implications of research which affect knowledge production in Mathematical Statistics; also demonstrate an ability to critically contribute to the development of ethical standards in Mathematical Statistical research.

Associated Assessment Criteria for Exit Level Outcome 1

• Identify and formulate a problem statement.
• Investigate the existing knowledge as reflected in appropriate scientific literature.
• Solve the problem research appropriately.
• Evaluate the results in the context of the problem statement.
• Communicate the results in the form of a mini dissertation, research report or dissertation.

Associated Assessment Criteria for Exit Level Outcome 2

• Apply natural science knowledge and methods (with emphasis on those of the specific discipline) to problems.

Associated Assessment Criteria for Exit Level Outcome 3

• Apply appropriate scientific methods and to evaluate the results delivered.
• Use computer software for calculations, modelling, simulation and handling of information.

Associated Assessment Criteria for Exit Level Outcome 4.

• Identify and solve open business and community problems.
• Identify and utilise applications.
• Work with common fundamental expertise across the boundaries of disciplines.

Associated Assessment Criteria for Exit Level Outcome 5.

• Plan and perform investigations and experiments by utilising scientific modelling techniques.
• Analyse, interpret and derive information from data.

Associated Assessment Criteria for Exit Level Outcome 6.

• Communicate effectively both orally and in writing with scientists (with emphasis on the specific discipline) and the community.
• Use appropriate structure, style and graphics and electronic support.

Associated Assessment Criteria for Exit Level Outcome 7.

• Apply methods of information communication for use by others, especially in the world of Natural and Agricultural Sciences and health sciences (with emphasis on those of Statistics).

Associated Assessment Criteria for Exit Level Outcome 8.

• Demonstrate a critical awareness of the necessity to act professionally and ethically and to assume responsibility within his/her limitations and skills, while he/she can make judgments according to knowledge and experience.

Integrated Assessment

The evaluation of learner's ability to use and apply specific methodological skills occurs in his/her critical review of current literature in the subject area and the formulation and execution of a research project. The learner is to report on it and generate an integrated dissertation of research findings. The supervisor assesses continuously during the writing of the dissertation. Examination of the dissertation happens through at least two examiners of which one is external, all experts in the field of specialisation. A supervisor may not be an examiner.