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SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Bachelor of Science Honours in Computer Science 
SAQA QUAL ID QUALIFICATION TITLE
125626  Bachelor of Science Honours in Computer Science 
ORIGINATOR
Regenesys Education (Pty) Ltd 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
CHE - Council on Higher Education  HEQSF - Higher Education Qualifications Sub-framework 
QUALIFICATION TYPE FIELD SUBFIELD
Honours 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  120  Not Applicable  NQF Level 08  Regular-Provider-ELOAC 
REGISTRATION STATUS SAQA DECISION NUMBER REGISTRATION START DATE REGISTRATION END DATE
Registered  EXCO 0639/26  2026-04-16  2029-04-16 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2030-04-16   2033-04-16  

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 the Bachelor of Science Honours in Computer Science is to enable learners to develop advanced skills and knowledge, spanning a wide range of computer science topics relevant to the 4th Industrial Revolution, thus enabling them to keep pace with the emerging technologies evolving all over the world.

The aim of this qualification is to enhance the skills of learners in the fast-paced world of computer science, allowing them to participate in a multitude of IT-related activities across a diverse range of industries, advising organisations, and streamlining their operational performance.

Learners will be able to enter the workforce, confident in both their technical skills and enhanced graduate attributes, or they may choose to proceed with further postgraduate studies towards a possible Masters or Doctorate Degree.

The curriculum and exit level outcomes of this qualification are structured to ensure comprehensive coverage of the necessary advanced skills in Computer Science, inclusive of Software Engineering, Artificial Intelligence, and Big Data Management, whilst offering the opportunity to expand into fields such as Network Management, Interface Design, Mobile Programming, and Cybersecurity. The integration of these fields aims to produce high-quality, technically competent, and innovative graduates able to engage holistically with technology to provide leadership to organisations as they enter a new era of technological economic development.

Upon completion of this qualification, qualifying learners will be able to:
  • Demonstrate a comprehensive and specialised knowledge of computer science principles, methodologies, and implications of ethical issues required to effectively design, implement, and evaluate computer-based solutions to various context-specific problems.
  • Illustrate a thorough knowledge of the practice of software engineering through the application of advanced programming languages and software development processes, methodologies, and tools to tackle complex engineering problems and tasks.
  • Illustrate a comprehensive understanding of the basic research skills required to perform specialised computer-science research, displaying the competencies needed to make sound judgements based on evidence and to contribute towards the scientific literature of computer development and programming.

    Rationale:
    The qualification has been designed to meet the increasing demand for high-quality, technically competent, and innovative learners to address the gap in both the local and international markets in meeting technological goals. This skills gap has been highlighted by the National Development Plan 2030 and the Department of Higher Education and Training's emphasis on STEM qualifications at NQF level 8. The supply of graduates with specialized skills in areas such as artificial intelligence, software engineering, and Big Data remains far below global benchmarks. This mismatch is compounded by high unemployment among graduates in non-technical fields, while industries in finance, healthcare, and manufacturing struggle to innovate without the necessary technical talent.

    This qualification will strengthen South Africa's knowledge economy by producing learners equipped to drive technological advancements, aligning with national strategies like the Presidential Commission on the 4th Industrial Revolution and the Green Economy Accord through sustainable AI and Big Data applications that improve resource use and support environmental monitoring. By enhancing workforce productivity and fostering innovation, it will contribute to economic growth, job creation in high-value IT roles, and South Africa's competitiveness in global digital markets.

    The typical learners likely to be attracted to this qualification comprise holders of a Bachelor's degree in Computer Science, Information Technology, Engineering, or cognate disciplines, frequently comprising early-career practitioners motivated to augment their competencies in response to sectoral disruptions. Qualifying learners will be positioned to access professional roles, including software developers, artificial intelligence specialists, data analysts, cybersecurity consultants, and systems architects, within industries such as technology enterprises, financial services, telecommunications, and public administration. In addition, learners may embark on advanced academic trajectories, such as master's degrees in artificial intelligence or cybersecurity, professional accreditations (e.g., AWS Certified Solutions Architect, Certified Information Systems Security Professional), or doctoral investigations in computational sciences.

    Learners will be equipped for advanced leadership positions as innovative technologists, exemplified by roles such as senior software engineers, artificial intelligence solution architects, and Big Data strategists, thereby enabling them to provide strategic counsel to organisations on digital transformation and to lead technology-enabled economic development endeavours. 
    		•

    LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
    Recognition of Prior Learning (RPL):
    Recognition of Prior Learning (RPL) may be applied for by applicants who do not meet the admission criteria for any qualification. Recognition of prior learning access into this qualification will be applied to learners who discontinued their honours degree or postgraduate diploma studies at the institution or other institutions.

    This RPL assessment process will only apply to learners who discontinued their studies at NQF 8 in a cognate qualification, and should it be determined that there is a 70 % correlation in modular content between the completed modules and the modules from the Regenesys modules.

    RPL for Accesses:
    From an institutional perspective, admission of applicants per qualification through an RPL route would not constitute more than 10 percent of the learner intake for the qualification. The institution's RPL Policy and Process determine the criteria and methodology used to assess prior learning. The institution applies recognition of prior learning in the scope of the SAQA policy principles as amended 2019:

    Access:
  • RPL offers an alternative access route into a qualification of learning, professional designation, or recognition in the workplace to those who do not meet the specified qualification entry requirements. In this regard, qualifications, part-qualifications and professional designations registered on the NQF must provide alternative entry requirements so that candidates can be admitted to the qualification, part qualification, or professional designation, through RPL.
  • The RPL process is multi-contextual and differs across contexts. It may be developed and implemented differently for the purposes of recognition in the context of the three NQF Sub-Frameworks, professional designations, and recognition in the workplace. Furthermore, it is conducted using a variety of specialised learning interventions and/or assessment approaches through which the knowledge, skills, and values of a person are made visible, mediated, and assessed.

    The purposes and contexts of RPL determine the practices and outcomes of the RPL process in each case.
  • The focus is on what has been learned, and not on the status of the institution or place where the learning was obtained.
  • Assessment is an integral feature of all forms of RPL and exists in combination with a range of other strategies that allow for different sources of knowledge and forms of learning to be compared and judged.

    RPL for credits:
    RPL includes diagnostic, formative, or summative assessments, to create opportunities for, or towards, access and/or credit.
  • Where credit is awarded, it must be based on the assessed evidence of the knowledge and skills acquired informally and non-formally.
  • There must be no distinction, other than that required for data analysis, between records of learner achievements for qualifications, part-qualifications or professional designations awarded as a result of RPL processes and those obtained via conventional means.

    The quality assurance of RPL must be undertaken with the explicit intention to protect the integrity of the processes and outcomes concerned.
  • The institution does not exceed the national maximum of 10 % admissions into its the qualification via the recognition of prior learning.

    Entry Requirements:
  • Bachelor of Science in Computer Science, NQF Level 7.
    Or
  • Bachelor of Information Technology, NQF Level 7. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory and elective modules at National Qualifications Framework, Level 8 totalling 120 Credits.

    Compulsory Module, Level 8, 90 Credits:
  • Artificial Intelligence in Context, 15 Credits.
  • Computer Science, 15 Credits.
  • Software Engineering & Advanced Programming, 15 Credits.
  • Big Data Management & Analysis, 15 Credits.
  • Computer Science Research Project, 30 Credits.

    Elective Module, Level 8, 45 Credits (choose two from the following)
  • Adaptive Computation and Machine Learning, 15 Credits.
  • Cybersecurity and Forensics, 15 Credits.
  • Mobile Programming, 15 Credits.
  • Computer Graphics and Interface Design, 15 Credits.
  • Network Management & Infrastructure Protection, 15 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1. Demonstrate a comprehensive and specialised knowledge of computer science principles, methodologies, and implications of ethical issues required to effectively design, implement, and evaluate computer-based solutions to various context-specific problems.
    2. Illustrate a thorough knowledge of the practice of software engineering through the application of advanced programming languages and software development processes, methodologies and tools to tackle complex engineering problems and tasks.
    3. Display an extensive knowledge of the theoretical and practical applications of Artificial Intelligence as an emerging technology, through the critical evaluation of the types of organisational problems and needs that may entail the building of intelligent systems, inclusive of the tools and techniques required to implement such systems.
    4. Demonstrate a comprehensive understanding of the basic research skills required to perform specialised computer-science research, displaying the competencies required to make sound judgements based on evidence and to contribute towards the scientific literature of computer development and programming.
    5. Prepare and analyse information housed within Big Data Systems to contribute towards informed decision-making processes, whilst taking into consideration various ethical, social, and data protection issues. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcome 1:
  • Accurately explain core computer science principles, methodologies, and ethical implications with reference to at least five peer-reviewed sources in a written report or equivalent presentation.
  • Design, implement, and evaluate a functional computer-based solution to a context-specific problem, such as an AI-driven application or big data pipeline, meeting predefined performance benchmarks (e.g., accuracy 85%, runtime 5 seconds).
  • Critically reflect on the ethical, societal, and professional implications of proposed solution in a portfolio, proposing at least three mitigation strategies justified by relevant frameworks like the Association of Computing Machinery (ACM) Code of Ethics.

    Associated Assessment Criteria for Exit Level Outcome 2:
  • Define and differentiate at least four advanced software engineering methodologies and their associated tools, with examples of application to complex problems, in an essay supported by current literature.
  • Apply advanced programming languages (e.g., Python, Java, or Rust) and development tools (e.g., Git, Docker, Jenkins) to independently develop, test, and deploy software solutions for a complex engineering problem.
  • Integrate software engineering processes to lead a team project tackling a multifaceted task, producing a comprehensive artefact (e.g., full-stack application) with documented lifecycle phases, stakeholder requirements met, and a reflective evaluation of process effectiveness.

    Associated Assessment Criteria for Exit Level Outcome 3:
  • Systematically describe and critique key theoretical foundations of AI (e.g., machine learning paradigms, neural networks, reinforcement learning) and their practical applications, citing at least six authoritative sources.
  • Identify and evaluate organisational problems that AI could solve, like predictive maintenance or customer sentiment analysis - selecting and justifying appropriate tools/techniques.
  • Design, implement, and critically evaluate a prototype intelligent system addressing a specified organisational need, using advanced AI techniques, documenting limitations, ethical considerations, and scalability in a technical portfolio.

    Associated Assessment Criteria for Exit Level Outcome 4:
  • Identify and apply core research skills (e.g., literature review, hypothesis formulation, experimental design) by synthesizing evidence from at least 10 peer-reviewed sources into a coherent research proposal on a computer science topic.
  • Conduct an independent research project using appropriate methodologies to gather and analyse data, producing verifiable evidence (e.g., empirical results, benchmarks) that supports sound judgements on a programming or development problem.
  • Produces a research project report, such as a conference-style paper, that aligns to all the requirements of a standard research initiative, demonstrating the ability to correctly apply a range of research methodologies and techniques to a research problem encountered within the Computer Science domain.

    Associated Assessment Criteria for Exit Level Outcome 5:
  • Explain the architecture, tools (e.g., Hadoop, Spark), and processes of Big Data systems, including ethical, social, and data protection frameworks (e.g., POPIA, GDPR), in a critical analysis supported by relevant case studies.
  • Prepare, process, and analyse a large-scale dataset (e.g., 1TB) using Big Data tools to extract useful insights, and present the results with clear visuals and performance measures to guide a simulated decision-making scenario.
  • Evaluate the implications of Big Data analysis outcomes on decision-making, proposing at least four ethical strategies to reduce social and privacy risks, documenting them in a reflective report that shows compliance with standards.

    INTEGRATED ASSESSMENT:
    The institution's assessment strategy broadly includes formative and summative assessments. For purposes of the formative assessment segment, learners are required to complete and submit two formal formative assessment tasks per module, i.e., an objective test (e.g., multiple choice, weighted at 20%) and a cognitive assessment (e.g., a traditional assignment, weighted at 40%).

    Learners are therefore encouraged to request a resubmission and ensure the submission of a greatly improved assessment. A re-submission is capped at 60% unless the Academic Committee is convinced that the reason for the re-submission is valid and compelling. The mark awarded after marking the resubmission will be the final recorded mark against the module, even if it is lower than the original mark.

    Upon achieving a sub-minimum of 40% on each formative assessment, the learners will be allowed to proceed to the summative assessment (weighted at 40%). The summative assessment will itself also have a minimum of 40%. The summative assessment carries a weight of 40 %. The final mark, therefore, is formed of the formative assessment carrying a weight of 60 % and the summative assessment carrying a weight of 40 %.

    A learner passes a module if a final average mark of at least 50% is achieved for the module as a whole, i.e. the final mark. This is subject to a sub-minimum of 40% being obtained for both the formative and summative assessments. A learner who does not achieve an average pass mark of 50% for the module after writing the exam may be granted the opportunity to improve and resubmit the assignment for which a mark of between 40-50% was achieved. Such a resubmission mark is capped at 60%.

    Those learners who obtained a final module mark of between 30% and 39% are not automatically allowed a second examination opportunity but may apply to the Academic Committee for special permission to write during the next examination session, providing written justification for the request. Weighting is 20 % for the digital assessment, 40 % or the individual assessment, and the summative assessment weighs 40 %.

    This strategy aligns with the broad sub-minimum approach and is seen to be part of the renewal of the institution's assessment approach as part of its plans to mitigate risks posed by the impact of external factors on learner success. The institution adopts a differentiated approach to assessment, allowing schools to design alternative assessment strategies.

    This allowance is afforded to schools in response to the recognition of the difference in context of application and nature of the qualifications and the qualifications offered, such as the NQF levels of the qualifications and qualification types, such as certificates, diplomas, degrees, and postgraduate diplomas and degrees. The strategies should, in all instances, align with the broad philosophy of a balanced approach to formative and summative assessment and should be well documented and communicated to learners via the relevant learner management platforms.

    The first assessment/s will be formative in nature and will therefore not necessarily be graded, as it will contribute towards the process of assessment for learning. If graded, it will contribute to the year mark.

    Summative Assessment:
    The summative assessment will take place upon completion of the module and will be designed to assess applied and reflective competencies based on an integrated combination of the assessment criteria related to the suite of specific outcomes of the module/course. All assessments will at all times adhere to outcomes-based assessment principles.

    Learners have to achieve a sub-minimum of 50 % in the summative assessment (or final mark if graded formative assessments form part of the year mark) to be considered as having successfully completed the module. In all events, should a learner achieve between 40% and 50 % in the modular summative assessment (or final mark), a second submission opportunity will be given. Learners have to achieve a sub-minimum of 50 % to be awarded a pass on the module.

    This strategy aligns with the broad sub-minimum approach and is seen to be part of the renewal of the institution's assessment approach as part of its plans to mitigate risks posed by the impact of external factors on learner success. 
    		•

    INTERNATIONAL COMPARABILITY 
    This qualification was compared with similar qualifications from New Zealand and Australia.

    Country: New Zealand.
    Institution: University of Auckland (UA).
    Qualification title: Bachelor of Science (Honours) in Computer Science.
    Duration: One year full-time.
    NZQF: Level 8.
    Credits: 120.

    Entry requirements:
  • Bachelor of Science with at least 60 points above Stage II.
    Or
  • Some specialisations may require you to have completed specific courses in your undergraduate degree and/or have a different GPA grade to the Minimum the qualification requirements.

    Qualification structure:
    Modules:
  • Phylogenetics (15 points).
    > Creating Maintainable Software (15 points).
    > Security for Smart-Devices (15 points).
    > Generalising Artificial Intelligence (15 points).
    > Fundamentals in Human-Computer Interaction (15 points).
    > Advanced Topics in Human Computer Interaction (15 points).
    > Special Topic (15 points).

    Similarities:
  • The University of Auckland (UA) and South African (SA) qualifications are at Level 8 of the respective qualification frameworks and are one-year qualifications with a minimum of 120 credits.
  • Both South Africa and New Zealand award 10 notional hours to one credit hour of study, and both countries' qualification frameworks have 10 levels, so in terms of regulatory aspects, the qualifications are entirely similar.
  • Both qualifications require a bachelor's degree as entry and learners can progress to a master's degree upon successful completion of the honour's degree.

    Country: Australia:
    Institution: University of New South Wales.
    Qualification title: Bachelor of Computer Science (Honours).
    Duration: One year full-time

    Purpose/rationale:
    Computer Science is the study of the design, construction, and use of computer systems. The Bachelor of Science (Honours) program in Computer Science and Engineering is a 1-year full-time or 2-year part-time award undertaken by eligible learners after completion of a 3-year Bachelor of Science program in a relevant discipline. It offers learners the opportunity to deepen their understanding of the discipline through advanced coursework and a research thesis.

    Entry requirements:
  • Completion of a 3-year Bachelor of Science in Computer Science or Bioinformatics at UNSW or equivalent, or completion of a Bachelor of Data Science and Decisions with a major in Computational Data Science at UNSW or equivalent.
    and
  • An overall weighted average (WAM) mark of 65 or higher.

    Qualification Outcome:
  • Demonstrate cognitive skills that review, analyse, consolidate and synthesize knowledge Scholars.
  • Identify and formulate solutions to complex problems with intellectual independence Professionals
  • Construct a research project that demonstrates critical thinking and judgement in developing a new understanding Global citizens
  • Construct a research project that demonstrates technical skills in research and design Professionals
  • Demonstrate an ability to adapt knowledge and skills in diverse contexts.

    Similarities:
  • Both the University of New South Wales and the South African (SA) qualifications are on Level 8 of the qualification frameworks.
  • Both qualifications are one-year the qualifications with 120 credits.
  • Both South Africa and Australia award 10 notional hours to one credit hour of study, and both countries' qualification frameworks have 10 levels, so in terms of regulatory aspects, the qualifications are completely similar.
  • Both qualifications require a bachelor's degree as entry and learners can progress to a master's degree upon successful completion of the honour's degree. 
    		•

    ARTICULATION OPTIONS 
    Horizontal Articulation:

    This qualification provides the following articulation options:
  • Postgraduate Diploma in Computer Science, NQF Level 8.
  • Bachelor of Science Honours in Computer Science and Information Systems, NQF Level 8.
  • Possible horizontal articulation options between Sub-Frameworks for this qualification reached the registration end date in December 2025.

    Vertical Articulation:
  • Master of Science in Computer Science, NQF Level 9.

    Diagonal Articulation:
  • Higher Occupational Certificate: Computer Technician NQF Level 5. 
    		•

    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.
     
    1. Regenesys Education (Pty) Ltd 


    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.