Master of Science in Risk Analytics

Purpose

The Master of Science in Risk Analytics aims to educate and train researchers who can contribute to the development of knowledge at an advanced level in the field of Risk Analytics.

The purpose of this qualification is to equip learners with advanced and integrated specialist knowledge and practical understanding of Risk Analytics, able to 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, thus, contributing towards the improved understanding of Risk Analytics. Successful completion of this qualification also provides learners with the possibility of further study at the Doctoral level.

Rationale

The qualification responds to the need to provide learners with advanced knowledge, specific skills and applied competence in the field of Risk Analytics. The learner with expert knowledge in Risk Analytics may enter the labour market equipped to make rewarding contributions to society through research activities.

The qualification will also deliver experts in the field of Risk Analytics to ensure that the national and international leadership base of innovative and knowledge-based scholarly activity is widened. This includes the requirement of high-level theoretical engagement, intellectual independence and research. Successful learners will be capable of undertaking all aspects of research, both individually and as part of a team, and may be able to continue with further postgraduate studies. Learners who complete this qualification will be employable nationally/internationally in different organisations as teachers, lecturers at universities and other higher education institutions, as researchers, etc. and be ready to commence with Doctoral studies.

Recognition of Prior Learning (RPL)

Where learners do not meet the minimum admission requirements, RPL may be used to grant access to the qualifications. RPL will be applied according to the Recognition of Prior Learning, credit accumulation and transfer, and assessment (CHE 2016) and the institution RPL policy. The process will be managed by the Faculty of Natural and Agricultural Sciences.

The Faculty of Natural and Agricultural Sciences will ensure that quality assurance processes that address the specificities of the RPL process (including applications, assessment, and reporting and management systems) are implemented; and that administrative and support systems, both prior and after RPL assessment, are in place. Learners will be assessed against NQF Level 8 competencies.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Commerce Honours in Information Risk Management, NQF Level 8.

Or

• Postgraduate Diploma in Personal Financial Risk Management, NQF Level 8.

This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 9 totalling 180 Credits.

Compulsory Modules, Level 9, 148 Credits

• Mini-Dissertation, 132 Credits.
• Research Methodology, 8 Credits.
• Research Communication, 8 Credits.

Elective Modules, Level 9, 32 Credits: (Select/Choose 2 only)

• Quantitative Risk Analysis I, 16 Credits.
• Financial Engineering, I, 16 Credits.
• Financial Modelling, I, 16 Credits.
• Practical Risk Management SAS RD, 16 Credits.
• Modern Portfolio Theory, 16 Credits.
• Practical Credit Risk, 16 Credits.
• Topical Research issues in Risk Analysis, 16 Credits.
• Enterprise-wide Risk Management I, 16 Credits.
• Quantitative Risk Analysis II, 16 Credits.
• Pricing of Derivatives A, 16 Credits.
• Financial Modelling Optimisation, 16 Credits.

1. Demonstrate specialist knowledge by engaging in current research and practices within the field of Risk Analytics; to 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 Risk Analytics.
3. Design, select and apply appropriate methods, techniques, procedures or technologies to complex practical and theoretical problems within Risk Analytics, with specific reference to their specialisation area.
4. Analyse complex research questions in the field of Risk Analytics 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 Risk Analytics.
7. Make recommendations regarding the findings of their research and how this relates to or can influence future research in their area of specialisation.
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 Risk Analytics.

8.1 Contribute to the development of ethical standards in Statistical research.

Associated Assessment Criteria for Exit Level Outcome 1

• Identify and formulate a problem statement.
• Conduct a thorough investigation of existing knowledge as reflected in the appropriate scientific literature.
• Select appropriate research to solve the problem.
• Scientifically evaluate the results in the context of the problem statement.
• Scientifically 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 the appropriate structure, style and graphic 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 (with emphasis on those of Risk Analytics).

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 judgements according to knowledge and experience.

Integrated Assessment

The learner's ability to use and apply specific methodological skills is evaluated 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 while the dissertation is being written. Examination of the dissertation is finally done by at least two examiners of which one is external, all experts in the field of specialization. A supervisor may not be appointed as an examiner.