Master of Science

Purpose

The main purpose of the Master of Science is to provide its graduates with the necessary skills and knowledge to identify, interpret, analyse, and solve complex problems to provide accurate and possible solutions in mathematical sciences and related fields such as engineering, health sciences, and other quantitative disciplines. The qualification will build the core mathematical and statistical skills common to all modern sciences, facilitate the flow of ideas and techniques across the disciplines involved provide learners with direct access to knowledge currently being generated and discussed at the forefronts of mathematical science, and finally further the creation of a network of mathematical scientists across Africa and globally.

On successful completion of this qualification, qualifying learners will be able to

• Apply advanced knowledge in mathematical sciences.
• Plan and execute a substantial research project.
• Apply precise oral and written communication skills for Mathematics or Statistics
• Highly develop computational skills using software such as MATLAB, R, mathematical, latex etc.
• Apply generated research knowledge in current situations.
• Critically evaluate and apply mathematical and statistical theories.

Rationale

The qualification will equip learners with a range of transferrable mathematical skills needed for employment and future research. The qualification has been designed to meet several employer-based requirements as well as to develop graduates with Mathematics or Statistics competencies. In Mathematics, these competencies and skills have been identified in alignment with the needs of potential employers. The graduates will be therefore equipped with the essential skills and knowledge that can be used in a variety of fields, some of which are: algebra, geometry, differential topology, complex manifolds, analytic methods in partial differential equations and Stochastic Calculus with Applications to non-Linear Filtering.

In Statistics, candidates will be equipped to focus on the relationships between groups of measurements, and similarities and differences, using probability theory and techniques derived from it. The qualification includes instruction in the principles of probability theory, distribution theory, time-series analysis, multivariate analysis, regression analysis, stochastic processes, Monte Carlo method, Bayesian statistics, non-parametric statistics, sampling theory, Biostatistics, and other statistical techniques.

Envisaged occupations for the graduates are

• Academics/ researchers.
• Actuarial analysts.
• Risk analysts / Business Analysts/ Credit Analysts.
• Investment banking.
• Management consultancy.
• Data analysts/ data scientists.
• Quantitative analysts.
• Machine Learning/ Artificial intelligence specialists.
• Statisticians.
• Mathematicians.

The candidate will develop a willingness to work collaboratively with other researchers in the same field or other fields requiring their expertise.

Recognition of Prior Learning (RPL)

The Recognition of Prior Learning in this qualification will be conducted in accordance with the Institution's Policy and Procedures for Recognition of Prior Learning (RPL) in line with the national policies for RPL, CAT and Assessment which states that only 10% of a cohort of learners can be admitted using the RPL route. RPL is regarded as one of the critical mechanisms for achieving redress and equity and accelerating access in the institution.

RPL for access

• This policy and set of procedures will be used in the admission of a learner to a learning programme where the minimum requirements have not been met (excluding the mature age exemption category of exemption for admission to undergraduate qualifications) but the candidate has evidence that the requirements can be met through RPL.
• All potential learners who are not in possession of an Honours degree in Mathematics/ Statistics need to provide proof of an equivalent qualification.
• Those who claim that they have achieved relevant prior learning which should give them access to the qualification need to apply to the Head of the Department of Mathematics for recognition of prior learning and the procedure will be followed according to the RPL policy.

RPL for credits

• Potential learners who want to be credited for prior need to apply in writing to the Head of the Department of Mathematics clearly stating the title(s) of the module or modules they want to be credited for, as well as the basis on which they believe the accreditation should be granted.
• The principles of RPL are subscribed to and will be implemented in a flexible manner.
• The principle of flexible and open access will, however, be offset against the provision that this should not be to the detriment of the potential learner and that his/her chances of success are reasonable.
• The Committee will assess the competencies of these learners to determine whether they are eligible or not.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Science Honours in Mathematics, NQF Level 8.

Or

• Bachelor of Science Honours in Statistics, NQF Level 8.

Or

• Bachelor of Science Honours in Mathematical Statistics, NQF Level 8.

This qualification consists of the following compulsory Dissertation at NQF Level 09 totalling 180 Credits.

Compulsory Modules, Level 9, 180 Credits

Dissertation in Mathematics, 180 Credits.

Or

Dissertation in Statistics, 180 Credits.

1. Demonstrate how the title is aligned with the research project.
2. Show how the conceptual and theoretical focus and quality of the literature study are relevant to the research conducted.
3. Demonstrate an understanding of research methods, techniques, and technologies to address research problems.
4. Show an ability to engage with current research in the discipline of Mathematics or Statistics research.
5. Show an understanding to comply with the technical, language and scholarly writing requirements/standards, e.g., referencing techniques as applied at the master's level.
6. Demonstrate ethical sensitivity when the research is conducted.

Associated Assessment Criteria for Exit Level Outcome 1

• Align the title cover to the topic meaningfully.
• Align the title with the central research problem/research questions/hypothesis.
• Meaningfully use the title in the demarcation of the research topic.

Associated Assessment Criteria for Exit Level Outcome 2

• Show adequate knowledge base in the conducted research dissertation in Mathematics or statistics.
• Apply the principles and theories in the field of study.
• Analyse and/or critique current research.
• Make sound theoretical judgments based on evidence.

Associated Assessment Criteria for Exit Level Outcome 3

• Align the dissertation's methodological orientation with the theoretical conceptual orientation of the study.
• Identify and apply the research methods, techniques, and technologies for addressing the research problem.
• Deal adequately with methodological challenges.
• Select, apply, and manage appropriate statistical software, instruments, and experimental techniques in the analyses of quantitative data or research samples.

Associated Assessment Criteria for Exit Level Outcome 4

• Identify, analyse, synthesise, and evaluate quantitative and/or qualitative data.
• Engage with current research in the discipline or field of study.
• Present and communicate academic/professional work appropriate to the context and level of the study.

Associated Assessment Criteria for Exit Level Outcome 5

• Adhere to technical, linguistic, and scholarly writing requirements/standards.
• Construct a meaningful and integrated argument in the dissertation.

Associated Assessment Criteria for Exit Level Outcome 6

• Elaborate on and show ethical sensitivity as well as evidence of how the research has been conducted in the dissertation.