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SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Master of Science in Data Science 
SAQA QUAL ID QUALIFICATION TITLE
115522  Master of Science in Data Science 
ORIGINATOR
University of KwaZulu-Natal 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
CHE - Council on Higher Education  HEQSF - Higher Education Qualifications Sub-framework 
QUALIFICATION TYPE FIELD SUBFIELD
Master's Degree  Field 10 - Physical, Mathematical, Computer and Life Sciences  Mathematical 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 0821/24  2019-12-17  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 
Purpose:
The Master of Science in Data Science aims to balance job ready learners who can immediately contribute to the needs of industry as we enter the 4th Industrial Revolution. Learners will accordingly be able to put the knowledge and critical thinking skills learnt in lectures into practice, integrating it with industry knowledge and practises, thus applying the skills learnt in a research project into practise in a realistic setting. Nationally there is a well- documented extreme shortage of high-level skills in Data Science, Big Data and data analytics in general. Data Science is the scientific study of the creation, validation and transformation of data to create meaning (Data Science Association). Data Science is thus an interdisciplinary field aimed at extracting knowledge from data in various forms, in order to "action the information" that is in the data.

This qualification aims to create the opportunity for further development of the high-end, scarce skills of Data Analytics. Learners of this qualification will thus respond to the need in industry for advanced Data Analytics skills, focused on Customer Intelligence Analytics and including Data-driven Internet-of-Things Analysis. Their training will thus focus on real world problems emanating from industry, where learners will learn how to manage, analyse, predict and classify data streamed from business and industry. By combining university-based lectures with real world projects through linking with industry, with strong support from an international grouping of similar units, learners of this programme will be able to contribute meaningfully to the national and global workplace. Thus a system will be created that is ideal for contributing in solving the crisis in high-end data analytics skills, so well evidenced and acknowledged nationally and internationally.

Learners will be able to work with vast amounts of data in industry, business and government, using appropriate techniques and analytical tools. They will be able to use appropriate software to carry out data analytics and solve complex problems. Their industry experiences will provide them with the skills to immediately be able to operate in the business environment on graduation. They will be independent learners, taking responsibility for their own learning, both from an academic and professional perspective, with a strong personal and work ethic, and a desire to contribute towards, and effect change in the community and wider work environment. Furthermore, they will contribute to research in Data Science in general as they will be able to design and execute a research study and effectively communicate the findings of their research

Rationale:
Southern Africa has an acute shortage of big real-world data, data science and skilled analytics resources. The industry C-level engagements in the financial, communications, retail and government sectors highlight the acute shortage, to the point of being an inhibitor to growth in these sectors. South Africa produces a significant number of theoretical research-focused Master of Science learners in statistics, with no real-world exposure to solving industry problems and little prospect of appropriate entry into the formal sector as "job-ready" learners. There is a disconnect between the skills developed by Statistics Departments and the skills required by industry to drive the economy. This qualification addresses this gap by producing learners analytically equipped for the task of deriving and effectively communicating actionable insights from a vast quantity and variety of data. 

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
Recognition of Prior Learning (RPL):
The institution considers the Council of Higher Education Policy on Recognition of Prior Learning and Credit Accumulation and Transfer and Assessment in Higher Education (2016) when admitting learners through RPL. Also, the institution's RPL Policy and specifically Rule GR7 (b) will guide some admissions and learners who have attained a level of competence considered adequate by the institution's Senate may gain access into Postgraduate studies in the institution. Therefore, in line with this policy, the Discipline of Statistics will assess the level of competence of prospective applicants through its internal structures before seeking the approval of the College Academic Affairs Board and the Senate for each learner.

Since all learners will have experience in the industry, the institution conducts RPL for access based on a submission of a portfolio of evidence of their work experience in Data Analytics, to show that the applicant has sufficient disciplinary learning in the field of Data Analytics. A fundamental principle that must inform RPL practice is that there is no compromise in terms of learning outcomes because of RPL practice (DHET, 2013, p. 18).

Entry Requirements:
  • Bachelor of Science Honours in related fields or equivalent, NQF Level 8.
    Or
  • Postgraduate Diploma in Data Science, NQF Level 8. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of compulsory modules at Level 9 totalling 192 Credits.

    Compulsory Modules, Level 9:
  • Data Reduction and Latent Variable Analysis, 16 Credits.
  • Optimisation and Recommendation Analysis, 16 Credits.
  • Research Methods for Data Science, 16 Credits.
  • Survival Analysis and Retention Modelling, 16 Credits.
  • LoT Analytics, 16 Credits.
  • Contemporary Topics in Business Analytics, 16 Credits.
  • Research Project in Data Science, 96 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1. Collect, explore and analyse industry, business and government data science techniques.
    2. Develop own learning strategies, which sustain independent learning and academic or professional development; and can interact effectively within the teaching or professional group as a means of enhancing knowledge.
    3. Derive and effectively communicate actionable insights from a vast quantity and variety of data.
    4. Efficiently use appropriate programming and software to carry out data analytics.
    5. Earn a practical hands-on experience that mirrors the day-to-day work of data analyst/scientist.
    6. Identify, analyse and address complex and abstract problems and produce papers or presentations/seminars on their findings.
    7. Ability to solve problems, through the use of statistical as well as machine learning packages.
    8. Demonstrate developed skills with a strong personality and work ethic, and a desire to contribute towards and effect change in the community and broader work environment.
    9. Operate independently and take full responsibility for own work, and, where appropriate, to account for leading and initiating processes and implementing systems, ensuring proper resource management and governance practices.
    10. Demonstrate the knowledge and understanding of IoT analytics process starting with data streaming.
    11. Use different tools of optimisation and recommendation methods and techniques.
    12. Demonstrate an appreciation for and knowledge of the theory and application of time to event.
    13. Demonstrate with hands-on experience in understanding when and how to utilise the multivariate data reduction techniques and latent variable analytic tools.
    14. Demonstrate with hands-on experience, knowledge and skills of advanced and recent topics of business data science analytics.
    15. Design a research study from its inception to its report.
    16. Demonstrate the ability to plan an industry project, to execute the research and to produce a coherent report.
    17. Select and use appropriate data software, and create a report in the proper format, using the language of the industry and the discipline. 

    ASSOCIATED ASSESSMENT CRITERIA 
    The following Associated Assessment Criteria applies across the Exit Level Outcomes:
  • Collect data and use it to analyse industry, business and government data science techniques through computer-based assignments learners.
  • Learn effectively and independently in tests, assignments and examinations.
  • Communicate actionable insights from vast quantities and variety of data in assignments, tests and exams learners.
  • Analyse data using appropriate programming and software.
  • Do work that shows skills equivalent to that of data analysts/scientists.
  • Identify and analyse complex problems in oral presentations and assignments.
  • Use statistical and machine learning packages to solve problems in assignments, tests and examinations.
  • Demonstrate a strong personal and work ethic, as well as willingness to contribute to effective change in communities is evident in all work produced.
  • Show leadership and the ability to work independently to ensure proper resource management and governance practices in their assignments, tests and exams.
  • Use techniques in assignments, tests and examination show that they have knowledge of and understand IoT analytics.
  • Show the ability to use tools of optimisation and recommendation methods and techniques.
  • Explain, define and describe the theory and application of time to event.
  • Use multivariate data reduction techniques and latent variable analytic tools.
  • Demonstrate knowledge of advanced and recent topics of business data science analytics and use these.
  • Understand the different stages of the research process.
  • Use knowledge to design a research project from inception to report.
  • Use knowledge of research methods to plan and execute a research project.
  • Select appropriate data software, and write the report using the proper language of industry and the discipline.

    Integrated Assessment:
    Assessment of learners is on all six taught modules and the research project. The assessment is through continual assessment, such as assignments, practical exercises and oral presentations, as well as final examinations. The assessment of the research project is on the resulting dissertation. 
    		•

    INTERNATIONAL COMPARABILITY 
    Institute for Advanced Analytics, North Carolina State University, USA Master of Science in Analytics.

    Entry requirements:
  • Bachelor's Degree from accredited U.S. College or university or its foreign equivalent;
  • Experience with coding in one or more languages;
  • Strong aptitude for complex quantitative analysis and academic success as evidenced by undergraduate coursework.

    Qualification structure:
    An intensive 10-month cohort-based course designed to immerse learners into the acquisition of practical knowledge and application of methods and techniques.

    Curriculum:
    Three compulsory courses (4 Credit each) and six elective courses (from nine available choices), four credit internship, and four credit project.

    Work-Integrated Learning:
    Within the course work, learners tackle genuine problems with data provided by industry and government sponsors using industry-standard tools.

    Assessment:
    Continuous Assessment and the final exam at the end of each course.

    Analytics and Data Science Institute, Kennesaw State University, Master of Science in Applied Statistic:
    Entry requirements:
  • Undergraduate degrees in the sciences or business.
  • GRE Score Report.
  • Completion of Calculus I & Calculus II.

    Qualification structure:
    A 22-month early evening qualification designed for professionals or learners with undergraduate Degrees in the sciences or business. The qualification is a 36 Credit hour (12 courses) applied graduate qualification designed to meet the needs of business, industry and government.

    Curriculum:
    Core courses of the qualification include optimisation, simulation modelling, probability modelling, data management and statistical methods.

    Work-Integrated Learning:
    The institution expects learners to complete an applied project based on data from their place of employment, from an internship or co-op experience or work done with a faculty member.

    Assessment:
    Continuous assessment and the final exam at the end of each course.
    Center for the Business Analytics University of Cincinnati, Master of Science in Business Analytics.

    Entry requirements:
  • Bachelor Degree equivalent to a US four year bachelor degree with at least a B average (3.0/4.0 system).
  • Three semesters, or four quarters, of college-level calculus through multivariate calculus. One course in linear algebra or matrix methods.
  • Fundamental knowledge of computing, including the facility in a procedural programming language like Ruby, Python, C, C++, Matlab, Java, Visual Basic, Pascal, or FORTRAN.

    Qualification Structure:
    The University of Cincinnati Master of Science in Business Analytics qualification may study either as full-time or part-time learners. Full-time learners can complete the qualification in as few as nine months. Late afternoon, evening and Saturday class sessions provide flexibility to part-time learners.

    Curriculum:
    Core courses of the qualification include optimisation, simulation modelling, probability modelling, data management and statistical methods.

    Work-Integrated Learning:
    Experiential-education opportunities for learners to work with top analytics companies through projects and internships.

    Assessment:
    Continuous assessment and the final exam at the end of each course.

    The qualification is comparable to the above international qualifications in the following areas:
    Entry requirements:
  • A suitable Honours Degree or a PGDip in Data Science.
  • Two years of experience in the industry.

    Qualification structure:
    One year part-time contact-based modules, followed by a dissertation in the second year of study.

    Curriculum:
    Six compulsory modules are covering topics like Data Reduction and Cluster Analysis, Optimisation and Data Warehousing. The dissertation must include a real-life situation in the industry.

    Work-Integrated Learning:
    Learners work in the industry, then the material must relate to problems in their daily work. Their dissertation is on an industry-related project. However, there is no formal placement in credit-bearing Work-Integrated Learning.

    Assessment:
    Continuous assessment, and a final examination in the course work modules and examination of the dissertation. 
    		•

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

    Horizontal Articulation:
  • Master of Science Level 9.

    Vertical Articulation:
  • Doctor of Science, 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.
     
    1. University of KwaZulu-Natal 


    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.