[Registered Qual & Unit Std Home page] [Search Qualifications] [Search Unit Standards]
SAQA All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source.
SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Master of Artificial Intelligence 
SAQA QUAL ID QUALIFICATION TITLE
118671  Master of Artificial Intelligence 
ORIGINATOR
University of Johannesburg 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
-   HEQSF - Higher Education Qualifications Sub-framework 
QUALIFICATION TYPE FIELD SUBFIELD
Master's Degree  Field 10 - Physical, Mathematical, Computer and Life Sciences  Information Technology and Computer Sciences 
ABET BAND MINIMUM CREDITS PRE-2009 NQF LEVEL NQF LEVEL QUAL CLASS
Undefined  180  Not Applicable  NQF Level 09  Regular-Provider-ELOAC 
REGISTRATION STATUS SAQA DECISION NUMBER REGISTRATION START DATE REGISTRATION END DATE
Reregistered  EXCO 0333/25  2025-07-10  2028-07-10 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2029-07-10   2032-07-10  

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 
Purpose:
The purpose of the Master of Artificial Intelligence is to develop skilled graduates able to work at a high level of competence with cutting edge Artificial Intelligence applications and concepts across a range of fields, including engineering, commerce and economics, mathematical sciences, natural sciences, and computer science. Graduates will be equipped with the skills, knowledge, and expertise in Artificial Intelligence (AI) research relevant to research, development, and practice in this field.

The qualification will also inculcate specialised AI training and promote lifelong learning as appropriate to the evolving discipline. The qualification will provide learners with insights into Psychology and AI, the importance of ethics, and a variety of aspects of machine learning etc. The focus on the research project will ensure graduates can undertake advanced reflection on contemporary issues and debates in the field.

Qualifying learners will be able to:
  • Follow and reproduce state-of-the-art AI research, and mathematically and practically grasp the AI key concepts.
  • Design and implement AI solutions to large-scale real-world business problems.
  • Understand key techniques in probabilistic learning and inference.
  • Design, create and apply AI solutions to problems about:
  • Visual image and video processing.
  • Time-series and complex, temporally varying data
  • Text corpora and language understanding.
  • Planning and control, Reinforcement Learning.
  • Understand the intersection of neuroscience and the frontiers of AI research.

    Rationale:
    There is an overwhelming need for Artificial Intelligence (AI) skills and training across South Africa and the globe. There is a high demand for specialised research and development skills, as well as applied skills in AI. AI is a key cross-cutting technological feature of the Fourth Industrial Revolution that has begun to penetrate all sectors of society, aspects of which are already evident in many aspects of work across a variety of sectors. As a result, research and development in AI are strongly associated with the context/s of its application.

    Everything from the financial services sector to healthcare now employs AI research techniques and development capabilities, and thus there is a range of skills, experience and knowledge required to capacitate current and future strategies in the various sectors. The qualification considers the wide-ranging - but specialised - skills, knowledge and experience that are and will be required of graduates by including modules that will serve as scaffolding in areas such as programming, ethics, research methodology, psychology etc. The qualification considers that these are the relevant knowledge bases required for developing the necessary graduate attributes.

    Furthermore, the envisaged learner intake will primarily include engineering, commerce and economics, mathematical sciences, natural sciences, and computer science disciplines, where each undergraduate qualification would not necessarily have had the specific knowledge base required but would have provided the graduate with a sufficient basis from which to acquire these next-level skills. The qualification is therefore designed to meet the needs of the envisaged learner intake and other stakeholders by bringing graduates from different disciplines into a multi-disciplinary space to produce well-rounded AI masters graduates. 
    		•

    LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
    Recognition of Prior Learning (RPL):
    RPL will be applied in line with the institution's policies and guidelines. The Faculty of Engineering and the Built Environment accepts Recognition of Prior Learning (RPL) as an integral part of education and academic practice. It is acknowledged that all learning has value and the Faculty will therefore endeavour to assess prior learning and award credit where relevant.

    The Faculty of Engineering and the Built Environment manages RPL according to the institution's RPL policy, which will be applied as follows for purposes of this qualification as set out in the Faculty of Engineering and the Built Environment policy:

    Through RPL a learner may gain access, or advanced placement, or recognition of status.
  • Recognition takes place in terms of requirements and procedures applied by the Faculty of Engineering and the Built Environment.
  • RPL in the case of a learner not complying with the formal entry requirements.
  • Is based on other forms of formal, informal, and non-formal learning and experience.
  • Is considered only where prior learning corresponds to the required NQF-Level.
  • Takes place where prior learning in terms of applied competencies is relevant to the content and outcomes of the qualification.
  • Is considered in terms of an assessment procedure that includes a motivated recommendation by an assessment panel to the Dean's Committee of the Faculty of Engineering and the Built Environment.

    Entry Requirements:
    The minimum entry requirement for this qualification is:
  • Honours Degree in Applied Mathematics/ Computer Science/Mathematics/ Statistics, NQF Level 8.
    Or
  • Four-year Bachelor's Degree in Electrical Engineering, NQF Level 8. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 9 totalling 183 Credits.

    Compulsory Modules, Level 9, 165 Credits:
  • Programming, 12 Credits.
  • Mathematics and Statistics for AI, 18 Credits.
  • Machine Learning, 18 Credits.
  • Research Methodology, 15 Credits.
  • Brain Morphology and Functionality, 6 Credits.
  • Psychology and AI, 18 Credits.
  • Ethics of AI, 18 Credits.
  • Research Project, 60 Credits.

    Elective Modules, Level 9, 18 Credits:
  • Computer Vision, 18 Credits.
  • Natural Language Processing, 18 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1 Design and create computer qualifications using high-level programming language for Artificial Intelligence applications.
    2. Critically evaluate and construct machine learning solutions for computational problems in the realm of applied Artificial Intelligence.
    3. Analyse, interpret and demonstrate knowledge of the factual frameworks of human anatomy, physiology, and psychology in the context of Artificial Intelligence.
    4. Creatively and innovatively research, investigate and analyse problems in a variety of Artificial Intelligence.
    5. Solve Artificial Intelligence-based problems using mathematical and statistical techniques and reasoning.
    6. Plan and conduct research applying appropriate theories and methodologies and perform appropriate data analysis and interpretation.
    7. Communicate effectively, both orally and in writing, with research audiences and the community at large, in so far as they are affected by the research and development, using appropriate data analysis and interpretation.
    8. Demonstrate and critically evaluate, where applicable, ethical sensitivity across a range of social and environmental contexts in the execution of Artificial Intelligence research and development activities, and apply critically thinking on fair, secure, and inclusive use of Artificial Intelligence applications in the contemporary African context. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcome 1:
  • Create high-level programming language using Python Plan and implemented in programs.
  • Correctly apply computer algorithms, flowcharts, and programming logic.

    Associated Assessment Criteria for Exit Level Outcome 2:
  • Describe and interpret accurately the theoretical concepts and main algorithms of machine learning.
  • Analyse and apply the main algorithms of machine learning.
  • Construct and evaluate machine learning models.

    Associated Assessment Criteria for Exit Level Outcome 3:
  • Evaluate and apply contemporary approaches in different sub-disciplines in psychological studies to AI, both theoretically and for interventions.
  • Examine and apply foundational knowledge and a factual framework of the anatomy and physiology of the brain and spinal cord and its related/associated structures.

    Associated Assessment Criteria for Exit Level Outcome 4:
  • Assess a research problem correctly and critically.
  • Demonstrate the effective application and integration of knowledge within the study.
  • Ability to cohesively link findings to applicable theoretical underpinnings.

    Associated Assessment Criteria for Exit Level Outcome 5:
  • Ability to solve vector- and matrix-based problems.
  • Evaluate eigenvalues and eigenvectors problems.
  • Formulate and critically analyse population inferences of sample observations.
  • Evaluate and solve constrained optimisation problems.

    Associated Assessment Criteria for Exit Level Outcome 6:
  • Identify and apply the appropriate methodologies for the project.
  • Ability to offer relevant theoretical underpinnings for their arguments.
  • Accurately interpret and analyse the findings/observations.

    Associated Assessment Criteria for Exit Level Outcome 7:
  • Ability to articulate their findings coherently.
  • Communicate with various stakeholders during all phases of the study.
  • Integrate the theoretical underpinnings with findings/observations in a cohesive manner.

    Associated Assessment Criteria for Exit Level Outcome 8:
  • Demonstrate an awareness of the social impact of their research, where relevant.
  • Obtain the required ethical clearance as necessary.
  • Demonstrate an awareness of the environmental impact of the research and development, where relevant.
  • Adhere and comply with Health and Safety legislation during the research and development activities, where relevant. 
    		•

    INTERNATIONAL COMPARABILITY 
    The outcomes, assessment, purpose and modules, degree of complexity and the notional learning time of this qualification have been favourably compared to similar qualifications from the following international institutions:

    Country: United Kingdom
    Name of Institution: Imperial College London
    Qualification title: Master of Science in Artificial Intelligence
    Duration: One year
    Credits: 90 credits
    Entry requirements:
  • First-class degree in mathematics, physics, engineering.
    or
  • Other degrees with substantial mathematics content.
    Purpose/Rationale:
    AI is a key growth area aiming, among other things, to automate the completion of highly complex tasks and increase productivity. As a result, AI has broad application in a variety of industries and is already a growing part of many existing industries. The specialist nature of this degree will provide learners with the skills to meet the needs of the industries that are recognising the transformative potential of AI, from healthcare to manufacturing to the automotive industry (driverless cars).

    Qualification structure:
    The qualification consists of the following compulsory and elective modules
  • Ethics, Privacy, AI in Society.
  • Introduction to Machine Learning.
  • Introduction to Symbolic Artificial Intelligence.
  • Software Engineering Practice and Group Project.
  • Python Programming.
  • Individual Project.

    Elective Modules (Select five modules):
  • Computational Finance.
  • Computational Optimisation.
  • Computer Vision.
  • Decentralised Finance.
  • Deep Learning.
  • Knowledge Representation.
  • Logic-Based Learning.
  • Mathematics for Machine Learning.
  • Modal Logic for Strategic Reasoning in AI.
  • Natural Language Processing.
  • Principles of Distributed Ledgers.
  • Probabilistic Inference.
  • Reinforcement Learning.
  • Robot Learning and Control.
  • Robotics.

    Assessment methods:
    Formative and summative assessments will include the following:
  • Coursework.
  • Laboratory exercises.
  • Laboratory-based examinations.
  • Paper-based examinations.

    Synopsis:
    The qualification is similarly structured to this new world-class Master of Science AI qualification. Core and elective modules are similar. Coursework program with research project component.

    Country: Malaysia
    Name of Institution: Asia Pacific University of Technology and Innovation
    Qualification title: Master of Science in Artificial Intelligence
    Duration: One-year full time and two years part-time
    Purpose/Rationale:
    This qualification is specifically designed to provide:
  • Advanced skills and techniques in artificial intelligence.
  • Research opportunities to solve meaningful industrial problems with artificial intelligence techniques.
  • Advanced research opportunities in artificial intelligence in preparation for doctoral studies.

    This qualification is geared towards practising IT/Computing professionals within the industry who seek further formal qualifications in Artificial Intelligence. In addition, professionals and managers who wish to enhance themselves with Artificial Intelligence knowledge and skills to postgraduate level will find this qualification attractive. Fresh undergraduate learners from an Artificial Intelligence / Software Engineering / Data Science background will also find this qualification worthwhile as a path to further enhance their academic qualifications.

    On successful completion of this qualification, learners will be able to:
  • Gain hands-on experience to implement Artificial Intelligence (AI) to solve problems.
  • Grasp knowledge on a wide range of subject matters ranging from Machine Learning, Robotics to Natural Language Processing.
  • Effectively undertake and manage large scale and complex Artificial Intelligence (AI) projects.
  • Engage in the design and implementation of Artificial Intelligence (AI) systems of high quality and reliability.
  • Appreciate problems and suggest solutions associated with the development of Artificial Intelligence (AI) systems.
  • Appreciate how an efficient Artificial Intelligence (AI) technology-based infrastructure is a key factor in enabling a business to gain a competitive edge.
  • Draw upon the body of knowledge and be able to overcome human limits to solve new problems using Artificial Intelligence (AI).

    in addition to their Master's Degrees, learners will receive a professional certificate from The Information Bus Company (TIBCO) Software Inc. TIBCO is amongst the global leaders in Integration, Data Management and Analytics platforms that has a global clientele. In addition to the certification, TIBCO, as APU's industry partner, has provided all learners & lecturers with complimentary access to the TIBCO Spotfire software for academic purposes. Learners are utilizing the software to perform tasks & projects related to data analytics.

    TIBCO certification is awarded to learners who complete:
  • Business Intelligence Systems.
  • Applied Machine Learning.
  • Deep Learning.

    Qualifying learners will be able to pursue the following career paths:
  • Software Engineer.
  • Data Scientist.
  • AI Researcher.
  • Intelligence Specialist.
  • Consultant.
  • AIl Data Analyst.
  • Machine Learning Engineer.
  • Robotics R&D Engineer.
  • Machine Vision Engineer.
  • Artificial Intelligence Analyst.
  • Deep Learning Scientist.

    Qualification structure:
    The qualification comprises three pre-requisite modules (for non-computing learners), ten modules including three elective modules and a project. Elective modules may be pre-selected for learners at the beginning of the semester. If learners wish to change these pre-selected elective modules, they can choose from the available modules offered in the semester OR among the intensive delivery modules - however, such changes may prolong the study duration.

    Pre - Requisite Modules:
    (For Non-Computing learners: To be completed on 1st Month of the Qualification)
  • Programming in Python.
  • Data Structures and Algorithms.
  • Fundamentals of Artificial Intelligence.

    Core Modules
  • Artificial Intelligence.
  • Image Processing and Computer Vision.
  • Fuzzy Logic.
  • Applied Machine Learning.
  • Computational Intelligence Optimization.
  • Natural Language Processing.
  • Research Methodology in Computing and Engineering.
  • Project.

    Elective Modules (Choose three modules):
  • Applied Robotics.
  • Pattern Recognition.
  • Expert Systems and Knowledge Engineering.
  • Business Intelligence Systems.
  • Multivariate Methods for Data Analysis.
  • Deep Learning.

    Learners will be expected to conduct effective research in relation to Artificial Intelligence for both academic and industry purposes. Either route will require learners to plan and conduct effective academic research, and produce one academic paper, consultancy report or academic paper in relation to an aspect of Artificial Intelligence.

    Synopsis:
    The new qualification is similarly structured to this world-class. MSc AI qualification. Core and elective modules are similar.

    Country: Australia
    Name of Institution: Monash University
    Qualification title: Master of Artificial Intelligence
    Duration: 2 years full-time, 4 years part-time
    Entry requirements:
  • Australian Bachelor's degree, not necessarily in IT, with at least a 65% average, or equivalent qualification approved by the faculty.
    Or
  • Australian Bachelor degree in a cognate discipline relating to IT, or an engineering or science degree with a substantial IT component including programming and mathematics, with at least a 65% average, or equivalent qualification approved by the faculty.
    Qualification structure:
    The course is structured in three parts: Part A. Foundations for advanced artificial intelligence studies, Part B. Core master's study, and Part C. Advanced practice.

    These studies will provide an orientation to the field of artificial intelligence at the graduate level. They are intended for learners whose previous qualification is not in a cognate field.

    These studies will provide an orientation and draw on best practices within the broad field of artificial intelligence practice and research. You will gain a critical understanding of theoretical and practical issues related to artificial intelligence. Your studies will focus on fundamentals, core knowledge as well as application in artificial intelligence.

    The focus of these studies is professional or scholarly work that can contribute to the portfolio of professional development in AI. Learners will have two options:
  • A program of coursework involving advanced study and an industry experience studio project.
  • A research pathway including a thesis. If learners wish to use this master's course as a pathway to a higher degree by research, they should take this option.

    Learners can exit this course early and apply to graduate with one of the following awards, provided they have satisfied the requirements indicated for that award during your enrolment in this master's course:
  • Graduate Certificate of Artificial Intelligence after successful completion of 24 credit points of study or
  • Graduate Diploma of Artificial Intelligence after successful completion of 48 credit points of study.

    The Master of Artificial Intelligence comprises 72 units structured into two parts.
    Part A: Foundations for advanced AI studies, 24 points Four units,18 points:
  • Programming foundations in Java.
  • Algorithms and programming foundations in Python.
  • Introduction to computer architecture and networks.
  • Mathematical foundations for data science and AI.

    Part B: Core master's studies, 48 points.
    Complete: A. Three units, 18 points.
  • Statistical data modelling.
  • Fundamentals of artificial intelligence.
  • IT research methods.
    B. Five units (30 points)
  • Machine learning.
  • Deep learning.
  • Modelling discrete optimisation problems.
  • Natural language processing.
  • Human-centric AI.
  • Advanced learning and cognitive systems.
  • Solving discrete optimisation problems.
  • Intelligence image and video analysis.
  • Planning and automated reasoning.
  • Multi agent systems and collective behaviour.

    Part C: Advanced practice, 24 points.
  • Industry experience.
  • Industry experience studio project.
  • Professional practice.

    One elective selected from any FIT level five units B.
  • Minor thesis research.
  • Master's thesis (three units).
    One elective selected from any FIT level five units.

    Synopsis
    The new qualification is similarly structured to this world-class Master of AI qualification. Core and elective modules are similar. Content: Coursework only or Coursework with a research project

    Country: United States of America
    Name of Institution: Carnegie Mellon University (CMU)
    Qualification title: Master of Sciences in Artificial Intelligence and Innovation (MSAII)
    Purpose:
    The qualification equips learners to identify potential artificial intelligence applications and develop and deploy AI solutions to large practical problems. Learners work in teams to implement AI systems responsive to market needs. The qualification trains professional master's learners in the design, engineering, and deployment of practical Artificial Intelligence applications while preparing them for intrapreneurial and entrepreneurial careers. In the qualification, learners receive rigorous training in machine learning and language technologies. Through core classes, knowledge requirements and electives, learners develop the skills necessary for them to develop innovative AI systems to solve real, practical problems.

    Qualification structure:
    The curriculum provides a thorough grounding in machine learning, neural networks, natural language processing and deep learning, in addition to critical business skills such as market intelligence, intrapreneurship and entrepreneurship.
    To earn the MSAII degree, learners must pass courses in the Core Curriculum, the Knowledge Requirements and Electives. Learners must also complete a capstone project in which they work on a development project as part of the Core Curriculum. In total, learners will complete 195 eligible units of study, including 84 units of Core Curriculum, including the 36-unit Capstone, 72 units of Knowledge Requirements, at least 36 units of approved Electives and the LTI Practicum (3 units, associated with the summer internship). The purpose of the Core Curriculum is to prepare learners to discover new AI applicants and develop them into a product suitable for further development, often leading to a startup enterprise. Below is the detailed breakdown of the curriculum.

    Preparation Prerequisite
    Historically, learners typically need a refresher on basic computer science systems before beginning graduate work at CMU. Learners must pass the undergraduate course Introduction to Computer Systems (6 units), typically in the summer before the qualification commences. This course is the distance education version of the Introduction to Computer Systems. Failure to pass the course means that learners must take it during either the fall or spring semester, and the units will not count toward your 192 eligible units of study.

    Curriculum Components
    Each major has different core curriculum requirements.

    Core Curriculum, 84 units
    This is a five-course sequence based on the four main phases of innovation development, including opportunity identification, opportunity development, business planning and incubation of a business with a viable product. The courses must be taken in the order listed:

    First fall semester
  • Artificial Intelligence and Future Markets, 12 units.
    Learners are divided into teams to survey the field of AI applications, make presentations to the faculty and fellow learners on areas that are ripe for AI development, and must develop a product proposal, which will be carried through for the next three semesters, leading to the Capstone Project.
  • Law of Computer Technology, 12 units.
    A review of legal principles applicable to computer developments, including AI law and the formation of start-ups.

    Second fall semester.
  • AI Innovation, 12 units.
    Learners learn how to build an enterprise, either intrapreneurial or entrepreneurial, by developing a business model and strategy for their team's product.

    First spring semester
  • AI Engineering,12 units.
    This course is devoted to building deep learning applications using TensorFlow and Python. Topics include supervised learning, feed-forward neural networks, flow graphs, dynamic computational graphs, convolutional neural networks, and recurrent neural networks. Learners will use high-level tools to engineer functioning machine learning models.

    Second spring semester.
  • Capstone Project, 36 units.
    The objective of the Capstone is for the team to develop a working product suitable for intrapreneurial integration into a company or suitable for start-up investment.

    Knowledge Requirements, 72 units.
    This is a set of six rigorous courses to ensure that learners develop advanced AI applications.
    First fall semester.
  • Coding Bootcamp, 12 units.
  • Machine Learning, 12 units.
  • Math for Machine Learning, 12 units.

    Second fall semester
    Natural Language Processing,12 units.

    First spring semester.
  • Machine Learning with Large Datasets,12 units.

    Second spring semester.
  • Deep Learning,12 units.

    Internship
    Every learner is required to complete an industry internship during the summer between the first spring and second fall semesters. Every learner must register for the internship - MSAII Practicum Internship). No tuition is charged for the internship.

    Elective, 36 units (Select three modules).
    Learners must take at least three 12-unit elective courses or equivalent. The approved electives are listed below. If learners want to take any other course for elective credit, they must have the permission of the MSAII Director. It is recommended to take one elective in the first fall semester, one or two in the first spring semester, one or two in the second fall semester and zero or one in the second spring semester.
  • Machine Learning for Text Mining, 12 units.
  • Search Engines, 12 units.
  • Neural Networks for NLP, 12 units.
  • Machine Learning for Signal Processing, 12 units.
  • Advanced Multimodal Machine Learning, 12 units.
  • Design of Intelligent Information Systems, 12 units.
  • Machine Learning with Large Datasets, 12 units.
  • Conversational Machine Learning, 12 units.
  • Advanced Machine Learning: Theory & Methods, 12 units.
  • Foundations of Cyber-Physical Systems, 12 units.
  • Database Systems, 12 units.
  • Practical Data Science, 12 units.
  • Advanced Cloud Computing, 12 units.
  • Graduate Artificial Intelligence, 12 units.
  • Computer Vision, 12 units.
  • Medical Image Analysis, 12 units.
  • Sensing and Sensors, 12 units.
  • Visual Learning and Recognition, 12 units.
  • Web Application Development, 12 units.
  • Management of Software Development, 12 units.
  • Methods: Deciding What to Design, 12 units.
  • Managing Software Development, 12 units.
  • Software Engineering for Start-ups, 12 units.
  • Fundamentals of Bioinformatics, 12 units.
  • Computational Medicine, 12 units.

    Comparison:
    The new qualification is similarly structured to this world-class MSAII qualification. Core and elective modules are similar in that the qualification offers a Coursework program with a capstone project component.

    Conclusion:
    The new qualification compares best with the cited international qualifications in that the exit level outcomes, purpose, and modules are similar. 
    		•

    ARTICULATION OPTIONS 
    This qualification allows possibilities for both vertical and horizontal articulation.
    Horizontal Articulation:
  • Master of Science in Building, NQF Level 9.
  • Master of Engineering in Computer and Electronic Engineering, NQF Level 9.
  • Master of Science in Machine Learning and Artificial Intelligence, NQF Level 9.
  • Master of Philosophy in Electrical and Electronic Engineering, NQF Level 9.
  • Master of Engineering in Chemical Engineering/ Civil Engineering/ Electrical and Electronic Engineering/ Engineering Management/Mechanical Engineering/Metallurgy Engineering/ Operations Management, NQF Level 9.

    Vertical Articulation:
  • Doctor of Engineering in Chemical Engineering/ Civil Engineering/ Electrical and Electronic Engineering/ Engineering Management/Mechanical Engineering/Metallurgy Engineering/ Operations Management, NQF Level 10.
  • Doctor of Engineering, NQF Level 10.
  • Doctor of Construction Management, NQF Level 10.
  • Doctor of Philosophy in Informatics, NQF Level 10.
  • Doctor of Quantity Surveying, NQF Level 10.
  • Doctor of Philosophy in Applied Mathematics, NQF Level 10.
  • Doctor of Philosophy in Applied Statistic, NQF Level 10.
  • Doctor of Philosophy in Computer and Information Sciences, NQF Level 10.
  • Doctor of Philosophy in Computer Science, NQF Level 10.
  • Doctor of Commerce in Econometrics, NQF Level 10.
  • Doctor of Commerce in Economics, NQF Level 10.
  • Doctor of Economics, NQF Level 10.
  • Doctor of Philosophy in Business and Information Management, NQF Level 10.
  • Doctor of Philosophy in Information Technology, NQF Level 10.
  • Doctor of Philosophy in Business Administration, NQF Level 10.
  • Doctor of Philosophy in Machine Learning and Artificial Intelligence, NQF Level 10. 
    		•

    MODERATION OPTIONS 
    N/A 

    CRITERIA FOR THE REGISTRATION OF ASSESSORS 
    N/A 

    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.
     
    NONE 


    All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source.