SAQA

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SOUTH AFRICAN QUALIFICATIONS AUTHORITY

REGISTERED QUALIFICATION:

Postgraduate Diploma in Science

SAQA QUAL ID	QUALIFICATION TITLE
9227	Postgraduate Diploma in Science
ORIGINATOR
University of Witwatersrand
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY			NQF SUB-FRAMEWORK
CHE - Council on Higher Education			HEQSF - Higher Education Qualifications Sub-framework
QUALIFICATION TYPE	FIELD		SUBFIELD
Postgraduate Diploma	Field 10 - Physical, Mathematical, Computer and Life Sciences		Life Sciences
ABET BAND	MINIMUM CREDITS	PRE-2009 NQF LEVEL	NQF LEVEL	QUAL CLASS
Undefined	120	Level 7	NQF Level 08	Regular-Provider-ELOAC
REGISTRATION STATUS		SAQA DECISION NUMBER	REGISTRATION START DATE	REGISTRATION END DATE
Registered		EXCO 0733/25	2024-06-30	2027-06-30
LAST DATE FOR ENROLMENT		LAST DATE FOR ACHIEVEMENT
2028-06-30		2031-06-30

In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise.

This qualification does not replace any other qualification and is not replaced by any other qualification.

PURPOSE AND RATIONALE OF THE QUALIFICATION

The purpose of a Postgraduate Diploma in Science is to provide advanced, career-focused knowledge and practical skills in a specialized area, serving as a bridge between a bachelor's degree and a master's degree. It aims to deepen a student's understanding of a subject, enhance problem-solving abilities for complex challenges, and prepare them for career advancement or further research-focused study. The qualification offers in-depth knowledge and practical, industry-relevant skills in a specific field, allowing for a more focused approach than a general bachelor's degree.

Upon completion of this qualification, qualifying learners will be able to:

Demonstrate knowledge of and engagement in an area at the forefront of different fields and related disciplines.

Apply, in a self-critical manner, learning strategies which effectively address his or her professional and ongoing learning needs and the professional and ongoing learning needs of others.

Use a range of specialised skills to identify, analyse, and address complex or abstract problems, drawing systematically on the body of knowledge and methods appropriate to different fields

Rationale:
The development of this qualification was driven by South Africa's need to strengthen its scientific community amidst global technological and environmental shifts. Designed for learners with foundational scientific knowledge, the qualification provides specialised research methods across disciplines.

The qualification will enable working professionals to undertake advanced reflection and development by means of a systematic survey of current thinking, practice and research methods in an area of specialisation.

The qualification may serve as a bridge to further master's level studies in some fields, as it equips learners for roles in their specific fields, thereby fostering innovation and sustainability in the country.

The qualification positions graduates for leading roles in various science-based professions and fuelling socio-economic growth.

Upon completion of this qualification, learners will acquire specialised knowledge and practical skills in the chosen field or sub-disciplines. The qualifying learners will position themselves for specialised science-based careers and make contributions to South Africa's scientific community.

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING

Recognition of Prior Learning (RPL):
The institution has an approved Recognition of Prior Learning (RPL) policy, which is applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications. RPL may be applied for access, credits from modules, and credits or towards the qualification.

RPL for access:

Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.

To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal, and/or informal learning to cope with the qualification expectations, should they be allowed entrance into the qualification.

RPL for exemption from modules:

Learners may apply via CAT to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

Entry Requirements:
The minimum entry requirement for this qualification is:

Advanced Diploma in a cognate field, NQF Level 7.
Or

Bachelor of Science, NQF Level 7.
Or

Bachelor of Science in Data Science, NQF Level 7.
Or

Bachelor of Science in a cognate field, NQF Level 7.

RECOGNISE PREVIOUS LEARNING?

QUALIFICATION RULES

This qualification consists of the following compulsoryand elective modules at National Qualifications Framework Level 8, totalling 120 Credits.

Enterprise Risk Management field:
Compulsory Modules, Level 8, 120 Credits:

Copulas and Dependence, 20 Credits

Enterprise Risk Management (ERM) Concept and Framework, 20 Credits

Extreme Value Theory, 20 Credits

King III Corporate Governance in South Africa and ERM Case Studies, 20 Credits

Multivariate Models and Financial Time Series, 20 Credits

Risk Measurement, Assessment and Application of Enterprise Risk Management (ERM), 20 Credits
Or

Data Science field:
Compulsory Modules, Level 8, 60 Credits

Programming for Data Scientists, 15Credits

Mathematics and Statistical Foundations of Data Science, 15 Credits

Applied Machine Learning, 15 Credits

Data Science in Practice, 15 Credits
And

Elective Modules, Level 8, 60 Credits (Choose five modules):

Social Media Analysis in and for the Global South, 12 Credits

Visualising African Societies with Data, 12 Credits

Data-intensive Computing, 12 Credits

Applications of Machine Learning in Chemistry, 12 Credits

Data Science in Education, 12 Credits

Health Analytics for Data Science, 12 Credits

Spatio-temporal Data Analytics, 12 Credits
Or

Innovation and Entrepreneurship field:
Compulsory Modules, Level 8, 120 Credits:

Innovation and Research Commercialisation, 30 Credits

Design Thinking for Innovation, 20 Credits

Venture Creation for Innovators, 10 Credits

Basic Business for Innovators, 10 Credits

Communicating Innovation, 10 Credits

Research Project in Innovation and Entrepreneurship, 40 Credits.

EXIT LEVEL OUTCOMES

1. Generate, explore, and evaluate options and possibilities for determining the scope, content and methodology of research which ensures alignment with the research objectives and the broader scientific context.
2. Assess the rationale behind the chosen scope, content, and methodology, detailing the specific criteria and considerations which influenced the decision.
3. Evaluate personal learning progress, pinpointing strengths, acknowledging weaknesses, and recognising areas for improvement.
4. Reflect on the ethical dimensions of the research process and introspect on personal discoveries and growth throughout the learning journey.
5. Interpret boundaries, interconnections, value, and knowledge creation systems of the chosen science discipline(s), -along with the skill to critically evaluate these aspects.
6. Demonstrate to peers and instructors a deep understanding of selected content areas and analyse how they relate within the chosen science discipline(s).

ASSOCIATED ASSESSMENT CRITERIA

Associated assessment Criteria for Exit Level Outcome 1:

Evaluate a diverse range of research scopes and methodologies to determine their applicability to the specified research objectives.

Formulate a systematic approach to explore various research content possibilities.

Assess the alignment of chosen scope, content, and methodologies with the broader scientific context.

Demonstrate clarity in understanding and aligning research objectives with the chosen methodology.

Associated Assessment Criteria for Exit Level Outcome 2:

Clearly articulate the criteria and considerations that guide the selection of the research scope, content, and methodology.

Provide a coherent rationale that evidences a logical flow in the decision-making process.

Relate the chosen scope, content, and methodology to the existing scientific literature or contexts.

Associated Assessment Criteria for Exit Level Outcome 3:

Exhibit a structured reflection on the personal learning progress.

Identify specific strengths and relate them to aspects of the learning journey.

Recognize and acknowledge personal areas of weakness and improvement.

Constructively suggest areas for improvement and potential strategies to address them.

Associated Assessment Criteria for Exit Level Outcome 4:

Assess and discuss potential ethical concerns in the research process.

Reflect on moments of personal discoveries, evidencing growth and transformation.

Exhibit understanding and application of ethical guidelines and standards related to the chosen science discipline(s).

Associated Assessment Criteria for Exit Level Outcome 5:

Analyse the interconnections and boundaries within the chosen discipline(s).

Appraise the value and contribution of the chosen discipline(s) to the broader scientific community.

Critically evaluate existing knowledge systems and processes within the chosen discipline(s).

Associated Assessment Criteria for Exit Level Outcome 6:

Present content areas with clarity, depth, and confidence to peers and instructors.

Draw connections between different content areas within the chosen discipline(s).

Exhibit analytical skills in evaluating and relating selected content areas to overarching themes or contexts within the disciplines(s).

INTEGRATED ASSESSMENT:
Integrated assessment of these criteria will be achieved by a variety of strategies, including written examinations, individual projects and assignments, seminar presentations, and field trips where relevant.

To promote, monitor, and measure student learning throughout a course.
A single assessment may count for more than 40% of the final mark unless there are special circumstances, in which case the permission of the Dean is required.

INTERNATIONAL COMPARABILITY

This qualification compares with the following international qualifications in terms of the range of competencies in the learning content offered.

The below compares with respect to the Data Science specialisation.

Country: United Kingdom
Institution: The University of Edinburgh
Qualification: Postgraduate Diploma in Data Science

Similarities:
Both programs offer a systematic approach to evaluating and selecting research methodologies. Also, both programs have an emphasis on rigorous analytical skills and understanding of data science principals.

Differences:
The international qualification has different electives compared to this degree. The international qualification is also a part time program and takes 4 years to complete, whereas this qualification is offered full time and part time and can be completed in 2 years.

Country: New Zealand
Institution: University of Auckland
Qualification: Postgraduate Diploma in Science

Similarities:
Both qualifications require qualifying learners to pursue advanced study in their specialist area, such as Data Science or Innovation and Entrepreneurship in the South African context whilst the New Zealand qualification offers Biological Sciences or Computer Science.

Also, both qualifications are 120 credits and can be offered as a one year of full-time study.

ARTICULATION OPTIONS

This qualification offers both possibilities of horizontal and vertical articulation.

Horizontal Articulation:

Bachelor of Science Honours, NQF Level 8.

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

Bachelor of Science Honours in Computer Science, NQF Level 8.

Bachelor of Science Honours in Data Science, NQF Level 8.

Vertical Articulation:

Master of Applied Data Science, NQF Level 9.

Master of Science, NQF Level 9, NQF Level 9.

Master of Science in Applied Mathematics, NQF Level 9.

Master of Science in Computer Science, NQF Level 9.

Master of Science in Data Science, NQF Level 9.

Diagonal Articulation
There are no diagonal articulation options available on the OQSF on levels 6 and 9.

MODERATION OPTIONS

N/A

CRITERIA FOR THE REGISTRATION OF ASSESSORS

N/A

REREGISTRATION HISTORY

As per the SAQA Board decision/s at that time, this qualification was Reregistered in 2006; 2009; 2012; 2015.

NOTES

N/A

LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION:

NONE

PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION:

This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here.

1.	University of Witwatersrand