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SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Bachelor of Data Science 
SAQA QUAL ID QUALIFICATION TITLE
117722  Bachelor of Data Science 
ORIGINATOR
Stellenbosch University 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
CHE - Council on Higher Education  HEQSF - Higher Education Qualifications Sub-framework 
QUALIFICATION TYPE FIELD SUBFIELD
National First Degree(Min 480)  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  480  Not Applicable  NQF Level 08  Regular-Provider-ELOAC 
REGISTRATION STATUS SAQA DECISION NUMBER REGISTRATION START DATE REGISTRATION END DATE
Reregistered  EXCO 0821/24  2020-09-29  2027-06-30 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2028-06-30   2034-06-30  

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 
Purpose:
The content within this qualification has been formulated to address the needs generated by the fourth industrial revolution. The purpose of this qualification is to develop learners who have multi-disciplinary skills set. The qualification will enable qualifying learners to transform findings from various data resources into actionable business strategies.

This qualification ensures that qualifying learners acquire broad theoretical, practical and methodological knowledge tailored to data science from Mathematics, Statistics, Computer Science and Computational Engineering, all foundational to the data cycle as well as knowledge in the disciplines of application through the electives.

Qualifying learners will be able to:
  • Manipulate data using a variety of software packages and data handling techniques to represent data efficiently and in the most informative way for decision-making purposes.
  • Use mathematical, computational and statistical methodology on small or big data sets to detect patterns relating to problem-solving and model selection.
  • Demonstrate in-depth knowledge in the foundational areas of the mathematical sciences amongst others for example Statistics, Computer Science and Mathematical Sciences.
  • Communicate mathematical ideas using numerical, graphical and symbolic representations to colleagues, researchers and clients.
  • Construct software systems that satisfy the computing needs of the specialist field.
  • Learn independently to use emerging technologies and computing concepts and be able to master new methodologies and technologies in the field of Data Science.
  • Be able to use foundational data science ideas to further strengthen theoretical concepts.
  • Verify the correctness and efficiency of algorithms, programmes, and system implementation and further value and safeguard the ethical use of data in all aspects of the data cycle.

    The learners will have the opportunity to develop knowledge, attitudes and skills to attain the qualification outcomes through a variety of pathways including the deliberate choice of programme structure and content, teaching methodologies, assessment; teaching philosophy and approach; collaboration with industry and the focus on learner engagement and deep learning.

    Rationale:
    Data Science is emerging as a field that is revolutionising science and industries alike. Work across nearly all domains is becoming more data-driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analysing them become available, more aspects of the economy, society, and daily life will become dependent on data. The continued transformation of work requires both a larger population with a basic understanding of Data Science and a substantial cadre of talented graduates with highly developed data science skills and knowledge, acquired through substantial coursework and practice.

    Qualifying learners will work in virtually every job sector and will serve in several roles, including operating the systems on which analyses are run, preparing data for analysis, defining and coordinating the analysis, visualising information, and supporting data-driven decision-making to uncover the hidden knowledge buried in the data. This is achieved by focussing in the different data-rich environments of Statistical Learning, Behavioural Economics, Computer Science, Geoinformatics, Statistical Physics, Statistical Genetics, Analytics and Optimisation and Applied Mathematics. There are many reports that industry finds itself constrained by today's relatively small supply of well-trained Data Science talent, and data scientist hiring demand has begun to increase rapidly; some projections forecast that approximately 2.7 million new Data Science positions will be available by 2020. Not only is the lack of data science talent an issue, but there is a lack of understanding about what a data scientist is and what types of tasks such an individual might perform. The need in South Africa is no different than in the rest of the world. The need is probably more severe in South Africa and Africa as a continent, being isolated from the first world economies of the world.

    The need for such a multi-disciplinary qualification was further justified after the recent fact-finding tour of high ranked American Universities during October 2018 by the management team of the institution. The key learnings report which was compiled after the visit emphasised the need and importance of offering an undergraduate qualification in Data Science. The job market in the USA demonstrated that the demand for data scientists is extremely high worldwide (Columbus, 2017) and South Africa in some aspects are already falling behind. The approach in designing an undergraduate qualification in Data Science which should be multi-disciplinary was evident in the key Learnings Report of 2018.

    Data science is inherently interdisciplinary. Working with data requires the mastery of a variety of skills and concepts, including many traditionally associated with the fields of statistics, computer science and mathematics. Data Science blends the pedagogical content from all three disciplines. By applying the concepts needed from each discipline in the context of data, the curriculum can now be both significantly streamlined and enhanced. The integration of courses, focussing on data problems from these data-rich environments is a fundamental feature of this qualification, will result in a synergistic approach to problem-solving. The qualification forms a strong but wider curriculum for a diverse cohort of learners who intend to obtain a qualification that is today highly desirable and will lead to many job opportunities. The qualification is also designed to attract learners with a strong fundamental background in Mathematics and with a desire to study in the Statistical and Mathematical disciplines (with a further focus in Statistical Learning, Behavioural Economics, Computer Science, Geoinformatics, Statistical Physics, Statistical Genetics, Analytics and Optimisation or Applied Mathematics). As Business and Industry require a larger contingent of trained and skilled data scientists, the advantages are immediate and clear. 
    		•

    LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
    Recognition of Prior Learning (RPL):
    The institution regulation for the Recognition of Prior Learning (RPL) and Credit Accumulation and Transfer (CAT) applies.

    RPL can be applied for admission to this qualification. Prospective learners should also meet the minimum requirement or offer evidence of proficiency deemed to be equivalent at the required performance level, for an RPL application to be considered.

    Learners may be exempted from modules that are deemed at least equivalent to the modules offered. It is the learner's responsibility to provide evidence of the module content, assessment and duration of the module if the module equivalence has not been determined previously (as per the faculty exception list). The Registrar's office keeps a record of modules that are deemed equivalent (informed by the departments) and for which learners can obtain credit recognition or transfer without submitting further evidence.

    Where an exemption of modules, admission, credit accumulation and transfer for modules based is considered, the RPL committee makes a final decision and communicates the decision to the learner and recording on the institution administration systems.

    Entry Requirements:
    The minimum entry requirement for this qualification is:
  • National Senior Certificate, NQF Level 4 granting access to Bachelor studies.
    Or
  • Senior Certificate, NQF Level 4 with endorsement.
    Or
  • National Certificate Vocational, NQF Level 4 granting access to Bachelor studies. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 6, 7 and 8 totalling 498 to 508 Credits (depending on the focal area).

    Compulsory Modules, Level 6, 192 Credits:
  • Calculus, 16 Credits.
  • Calculus and Linear Algebra, 16 Credits.
  • Data Science 14X, 16 Credits.
  • Introductory Computer Science 1, 16 Credits.
  • Introductory Computer Science 2, 16 Credits.
  • Probability Theory and Statistics, 16 Credits.
  • Advanced Calculus and Linear Algebra, 16 Credits.
  • Computer Architecture, 16 Credits.
  • Data Science 24X, 16 Credits.
  • Data Structures and Algorithms, 16 Credits.
  • Distribution Theory and Introduction to Statistical Inference, 16 Credits.
  • Linear Models in Statistics, 8 Credits.
  • Statistical Inference, 8 Credits.

    Elective Modules, Level 6: Select a minimum of 24 Credits according to the focal area:
  • Cell Biology, 16 Credits.
  • Discrete Mathematics, 16 Credits.
  • Economics, 12 Credits.
  • Economics, 12 Credits.
  • Introductory Physics A, 16 Credits.
  • Introductory Physics B, 16 Credits.
  • Modelling in Mechanics, 16 Credits.
  • Theory of Interest, 8 Credits.

    Elective Modules, Level 6: Select a minimum of 32 Credits according to the focal area:
  • Analysis and Linear Algebra, 16 Credits.
  • Applied Differential Equations, 16 Credits.
  • Applied Matrix Methods, 16 Credits.
  • Classical Mechanics, Wave Theory and Optics, 16 Credits.
  • Digital Photogrammetry, 16 Credits.
  • Earth Observation, 16 Credits.
  • Economics A, 16 Credits.
  • Economics B, 16 Credits.
  • Electromagnetism and Introduction to Quantum Physics, 16 Credits.
  • Geographical Information Systems, 16 Credits.
  • Introductory Genetics, 16 Credits.
  • Introductory Molecular Biology, 16 Credits.
  • Linear Programming, 16 Credits.
  • Network Optimization, 16 Credits.
  • Spatial Data Management, 16 Credits.

    Compulsory Modules, Level 7, 80 Credits:
  • Data Science 31X, 16 Credits.
  • Data Science 34 X, 16 Credits.
  • Machine Learning, 16 Credits.
  • Program Design, 16 Credits.
  • Statistical Inference and Probability Theory, 16 Credits.

    Elective Modules, Level 7: Select a minimum of 48 Credits according to the focal area:
  • Advanced Topics in Molecular Genetics, 16 Credits.
  • Applied Discrete Mathematics, 16 Credits.
  • Applied Fourier Analysis, 16 Credits.
  • Combinatorial Optimisation, 16 Credits.
  • Computer Networks, 16 Credits.
  • Concurrency, 16 Credits.
  • Databases and Web-Centric Programming, 16 Credits
  • Earth Observation, 16 Credits.
  • Economics A, 16 Credits.
  • Economics B, 16 Credits.
  • Genomes and Genome Analysis, 16 Credits.
  • Methods in Operations Research, 16 Credits.
  • Molecular Population Genetics, 16 Credits.
  • Numerical Methods, 16 Credits.
  • Optimisation, 16 Credits.
  • Quantitative Genetics, 16 Credits.
  • Quantum Mechanics A, 16 Credits.
  • Regression and Predictive Modelling, 16 Credits.
  • Simulation and Inference in Stochastic Systems, 16 Credits.
  • Spatial Analysis, 16 Credits.
  • Spatial Data Acquisition, 16 Credits.
  • Spatial Modelling, 16 Credits.
  • Statistical Physics A, 16 Credits.
  • Stochastic Processes and Statistical Learning, 16 Credits.
  • Time Series Analysis, 16 Credits.

    Compulsory Modules, Level 8, 52 Credits:
  • Data Science Research Assignment 4X, 40 Credits.
  • Introduction to Statistical Learning, 12 Credits.

    Elective Modules, Level 8, Select a minimum of 70 to 80 Credits according to the focal area:
  • Advanced algorithms, 16 Credits.
  • Advanced Cross-section Econometrics, 20 Credits.
  • Advanced Linear Programming, 16 Credits.
  • Advanced Remote Sensing, 30 Credits.
  • Advanced Time Series Econometrics, 20 Credits.
  • Applied Markov Processes, 16 Credits.
  • Bayesian Physics, 8 Credits.
  • Bayesian Statistics, 12 Credits.
  • Behavioural Economics, 10 Credits.
  • Bioinformatics, 8 Credits.
  • Computer Vision, 16 Credits.
  • Digital Image Processing, 16 Credits.
  • Dynamical Systems and Complexity, 8 Credits.
  • Functional Programming, 16 Credits.
  • Genetic Data Analysis, 8 Credits.
  • Genetics: Molecular Techniques, 16 Credits.
  • Genomics A, 8 Credits.
  • Genomics B, 8 Credits.
  • Geographical Information Science, 30 Credits.
  • Geographical Information Science Research Application, 30 Credits.
  • Graph Theory, 16 Credits.
  • Human and Animal Genetics, 8 Credits.
  • Lagrange and Hamilton Mechanics, 16 Credits.
  • Machine Learning, 16 Credits.
  • Macroeconomics, 12 Credits.
  • Metaheuristics, 16 Credits.
  • Methods of Operations Research, 16 Credits.
  • Microeconomics, 12 Credits.
  • Multivariate Statistical Analysis A, 12 Credits.
  • Multivariate Statistical Analysis B, 122 Credits.
  • Numerical Methods, 16 Credits.
  • Plant Genetics and Crop Improvement, 8 Credits.
  • Scientific and Proposal Writing, 8 Credits.
  • Software Verification and Analysis, 16 Credits.
  • Space Science Algorithms, 16 Credits.
  • Spatial Modelling and Geographical Communication, 30 Credits.
  • Statistical Physics B, 16 Credits.
  • Stochastic Simulation, 12 Credits.
  • System Dynamics, 16 Credits.
  • Time Series Analysis, 12 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1. Manipulate data using a variety of software packages and data handling techniques to represent data efficiently and in the most informative way for decision-making purposes.
    2. Use mathematical, computational and statistical methodology on small or big data sets to detect patterns relating to problem-solving and model selection.
    3. Demonstrate in-depth knowledge in the foundational areas of the mathematical sciences amongst others for example Statistics, Computer Science and Mathematical Sciences.
    4. Communicate mathematical ideas using numerical, graphical and symbolic representations to colleagues, researchers and clients.
    5. Construct software systems that satisfy the computing needs of the specialist field.
    6. Learn independently to use emerging technologies and computing concepts and be able to master new methodologies and technologies in the field of Data Science.
    7. Use the foundational data science ideas to further strengthen theoretical concepts.
    8. Verify the correctness and efficiency of algorithms, programmes, and system implementation and further value and safeguard the ethical use of data in all aspects of the data cycle. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcome 1:
  • Generate visual and numerical data summaries using Excel.
  • Perform basic data cleaning to facilitate analysis.
  • Translate data into clear and actionable insights to support decision making.
  • Have substantial skills in Linux to manage files and data.
  • Assess and compare various models.
  • Interpret the results of machine learning techniques on data sets.
  • Draw graphs of simple multivariable functions, such as quadric surfaces.
  • Perform exploratory and descriptive analysis of data.
  • Compute expected values, variances and moment generating functions of continuous statistical distributions as well as bivariate continuous statistical distributions.

    Associated Assessment Criteria for Exit Level Outcome 2:
  • Fit a linear model in Excel.
  • Identify clear business objectives that need to be answered by analysing Big Data.
  • Discover relevant information in text by transforming the text into data that can be used in Natural Language Processing.

    Associated Assessment Criteria for Exit Level Outcome 3:
  • Define fundamental data science concepts.
  • Describe the data cycle and the cross-industry process (CRISP) data mining process.
  • Recognise and discuss ethical and legal issues inherent in data science.
  • Successfully collaborate as a Data Scientist in a team with other experts.
  • Explain the importance and the demand for the use of Big Data analytics in Business.
  • Identify clear business objectives that need to be answered by analysing Big Data.
  • Model patterns in complex datasets.
  • Show fundamental knowledge of the theory underlying statistical learning techniques.
  • Apply statistical learning techniques in a programming environment.
  • Explain the various approaches to the "curse of dimensionality" problem and how these approaches compromise between underlying assumptions and sample size requirements.
  • Explain the important role played by more traditional, established statistical procedures such as multiple regression analysis, logistic regression analysis and linear discriminant analysis in modern data mining.
  • Answer and or sensibly discuss simple theoretical questions and more complex theoretical questions about the properties of machine learning techniques.
  • Describe, define and prove the common methods of advanced distribution theory as well as several sampling distributions and apply it to theoretical and practical problems.
  • Define and prove the general methods of limit distributions of stochastic sequences and apply it to practical problems.
  • Define and apply the different approaches to inference.
  • Describe, define and prove the general methods within the Bayesian approach to inference as well as the application thereof on theoretical as well as practical problems.
  • Explain the fundamental theorems of vector calculus and their application.
  • Use the techniques of integration in multiple integrals.
  • Interpret the basic algorithms of linear algebra in terms of matric multiplication and matrix factorization.
  • Use eigenvalues and eigenvectors.

    Associated Assessment Criteria for Exit Level Outcome 4:
  • Demonstrate effective written communication skills that facilitate the presentation of data analysis results.
  • Visually analyse data to summarise its main characteristics.
  • Communicate solutions to data-related problems to interested parties using graphical, statistical and language-based skills in verbal and written presentations.
  • Interpret and effectively communicate results.
  • Conduct a relevant literature study on a selected topic or problem.
  • Execute the applicable research methodology to address the research problem.
  • Present results and computer code which are reproducible and ethical.
  • Write a structured scientific document that includes the following components: problem statement, literature study, research methodology, findings and discussion.
  • Apply the applicable reference system for scientific writing in the field of study.
  • Present an oral report on the project.
  • Present the results obtained when suitably applying machine learning techniques.

    Associated Assessment Criteria for Exit Level Outcome 5:
  • Evaluate the performance of different models applied to Big Data and text data.
  • Assess and compare various models.
  • Program in Python, using Numphy and Mat Plotlib.
  • Implement a variety of machine learning techniques successfully given specifications.
  • Make good use of Python's available facilities to implement machine learning techniques elegantly and efficiently.
  • Reconcile conflicting project objectives, finding acceptable compromises within limitations of cost, time, knowledge, existing systems and organisations.
  • Work alone and in a team to develop quality software.

    Associated Assessment Criteria for Exit Level Outcome 6:
  • Design and implement algorithms to solve small data-related problems.
  • Aggregate data from different sources to prepare for analysis.
  • Learn new models/techniques/technologies as they emerge and appreciate the necessity of ongoing professional development.

    Associated Assessment Criteria for Exit Level Outcome 7:
  • Apply Big Data analytical tools and techniques to help in providing meaningful information for making better business decisions.
  • Implement transformations to random variables to find the transformed probability distribution.
  • Apply the central limit theorem and sampling distributions in estimation via confidence intervals and hypothesis tests.

    Associated Assessment Criteria for Exit Level Outcome 8:
  • Demonstrate knowledge and appreciation for the importance of negotiation, effective work habits, leadership and good communication with stakeholders in a typical software development environment.

    Integrated Assessment:
    Number and types of assessments may vary between modules. However, each module follows the policies and rules of assessment of the institution. The two systems are either (1) flexible assessment, or (2) examination. The Assessment task team is currently working on the evaluation of assessment at the institution.

    Flexible assessment (in terms of the determination of a final mark) is a process by which a learner's progress in a semester- or year-module is systematically assessed and weighed through consecutive opportunities during the semester/year using a variety of assessment methods e.g. assignments, tests, portfolios, orals, laboratory investigations, seminars, tutorials, project reports, etc. (depending on the specific requirements and outcomes of the module). These may include both summative and formative assessments. A final mark is awarded without concluding the study period with a formal university examination.

    In the examination assessment system, learners write a semester test(s) to obtain a classmark that allows entrance into the examination. There are two examination opportunities, and the learner may choose which one to write. The classmark and examination mark are combined in a 40:60 ratio to obtain the final mark for the module. Both the test and the examination allow for a second opportunity in case of medical reasons.

    Formative and summative assessment opportunities: The practicals and tutorials serve as the formative assessment components of each module, while the tests and examinations serve as the summative assessment component. The tutorial tests are further summative assessment opportunities that contribute to the final mark in both the examination system and the flexible assessment system of the institution. Learners are expected to engage with the learning material and take responsibility for their learning. They are supported and offered numerous opportunities to determine their proficiency through low stakes assessment opportunities (formative learning). 
    		•

    INTERNATIONAL COMPARABILITY 
    The 2016 Park City Mathematics Institute (PCMI) in the USA, sponsored by its National Science Foundation (NSF) and the Institute for Advanced Study at Princeton (IAS), held a workshop for undergraduate faculties focused on the task of producing curriculum guidelines for an undergraduate degree in Data Science (Annual Review, 2016). Twenty-five faculties, comprising of computer scientists, statisticians and mathematicians from a variety of universities, as well as the established and traditional research universities, met for three weeks to discuss their vision for Data Science in an undergraduate context, what activities and skills they thought would be necessary for a Data Science undergraduate qualification and how they could imagine implementing such a qualification both currently and in the future.

    A 2018 report of the National Academies of Science, Engineering and Medicine, outlined the need for an undergraduate qualification in Data Science. Many Universities in the USA and those in Europe have introduced the qualification in Data Science. Universities like Berkeley, Stanford (Stanford Data Science initiative, 2018) and Columbia are following degree qualifications outlined in the Park City proposal.

    In Europe, the University of Essex, UK as well as Wittenberg University in Germany have introduced either a three-year or four-year undergraduate qualification with great success. It is important to note that all well-rated universities require both theoretical and practical exposure to real-life problems from data-rich environments. Most of these institutions require a four-year qualification while three-year qualifications are the exception rather than the rule. A Data Science qualification should therefore exit at NQF level 8, to ensure that sufficient depth and higher learning have been acquired.

    Country: United States of America
    Institution: Berkeley University
    Qualification Title: Bachelor of Arts in Data Science

    The qualification equips learners to draw sound conclusions from data in context, using knowledge of statistical inference, computational processes, data management strategies, domain knowledge, and theory. Learners learn to carry out analyses of data through the full cycle of the investigative process in scientific and practical contexts. Learners gain a deep appreciation of the human, social, and institutional structures and practices that shape technical workaround computing and data, as well as an understanding of how data, data analytics, machine learning, artificial intelligence, and computing permeate and shape our individual and social lives.

    Country: United States of America
    Institution: Stanford University
    Qualification Title: Bachelor of Arts in Data Science

    The Data Science minor has been designed for majors in the humanities and social sciences who want to gain practical know-how of statistical data analytic methods as it relates to their field of interest. The minor will provide learners with the knowledge of exploratory and confirmatory data analyses of diverse data types (e.g. text, numbers, images, graphs, trees, binary input); strengthen social research by teaching learners how to correctly apply data analysis tools and the techniques of data visualization to convey their conclusions. No previous programming or statistical background is assumed.

    Learners must be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis to be knowledgeable about programming abstractions so that they can later design their computational inferential procedures.

    All modules for the minor must be taken for a letter grade where offered, except for the data mining requirement. Seven modules are required, equal to at least 22 units. A grade point average (GPA) of 2.75 is required for courses fulfilling the minor.

    Country: United Kingdom
    Institution: University of London
    Qualification Title: Bachelor of Science Data Science and Business Analytics

    This is a 3-year online qualification that equips learners with essential skills in mathematics, statistics and economics, and can analyse data and draw actionable insights for informed decision-making, leverage mathematical and statistical models to tackle real-world problems; and navigate the intersection of business, management and data.

    Country: Germany
    Institution: Wittenburg University
    Qualification Title: Bachelor of Data Science

    This is a 4-year qualification that allows learners to gain expertise in scientific methods, processes and systems to extract knowledge from data in various forms, and it unifies statistics and data analysis to more powerfully understand the occurrences in question.
    Learners will be able to:
  • develop relevant programming abilities.
  • demonstrate proficiency with statistical analysis of data and develop the ability to build and assess data-based models.
  • execute statistical analyses with professional statistical software and demonstrate skill in data management.
  • apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively.

    All these qualifications require a core set of modules from Mathematics, Statistics and Computer Science as well as more general electives from different fields, and additional soft skill modules (Covering Topics in Data Ethics, Data Curation, Data Cleaning, Visualisation, Data Management and Presentation Skills) in Data Science applications complement the core and elective modules of these qualifications which is similar to the South African qualification.

    The purposes of these qualifications are closely comparable to the South African qualification in that it will enable qualifying learners to transform findings from various data resources into actionable business strategies. 
    		•

    ARTICULATION OPTIONS 
    This qualification allows possibilities for both vertical and horizontal articulation.

    Horizontal Articulation:
  • Bachelor of Commerce in Economic Sciences, NQF Level 7.
  • Bachelor of Science in Geo-Informatics, NQF Level 7.
  • Bachelor of Science in Mathematical Sciences, NQF Level 7.

    Vertical Articulation:
  • Master of Science in Computer Science, NQF Level 9.
  • Master of Science in Applied Mathematics, NQF Level 9.
  • Master of Commerce in Economics, NQF Level 9. 
    		•

    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. Stellenbosch University 


    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.