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SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Master of Science in eScience 
SAQA QUAL ID QUALIFICATION TITLE
117687  Master of Science in eScience 
ORIGINATOR
University of Limpopo 
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  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 0821/24  2020-09-16  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 qualification aims to prepare learners in the disciplines of Computer Science, Statistics, Mathematics and Applied Mathematics, to gain an interdisciplinary perspective on the emerging fields of e-Science, for the growing areas of e-Science affected by advances in cyberinfrastructure (computers/networks; data analytics/visualisation; data collection and storage, etc.).

The qualification will create opportunities for these learners to gain an interdisciplinary perspective on the emerging fields of e-Science. These opportunities will contribute to national and international priorities, such as improving service delivery and developing a competitive knowledge economy. It will create prospects for learners as professional researchers in academia and the public and private sectors. These developments are in line with the institution's vision which is to be a leading African University focused on the developmental needs of its communities and epitomising academic excellence and innovativeness.

Rationale:
Contemporary research, education and innovation are increasingly being impacted by advances in cyberinfrastructure, i.e. computers and networks; analytics and software, visualisation technologies; data collection and storage technologies. People are experiencing the impact of technology on research across all disciplines. Research has become more global, collaborative, complex and computational, data and network-driven. eResearch and e-Science tools are becoming more sophisticated and simultaneously more accessible, and the trend towards open data and extensive research is gaining momentum.

The qualification seeks to respond to national strategic priorities such as improved service delivery and developing a competitive knowledge economy. It is imperative that South Africa actively participates in be amongst the leaders in empowering local researchers in e-Research/eScience to participate fully in international projects that are locally relevant.

Key to responding to these challenges and being positioned to seize the presented opportunities is that South Africa grows a cohort of people skilled in a nascent combination of computational and data sciences superimposed on domain science expertise. In recognising this e-Research/e-Science skills gap, the 2012 NICIS Report recommended the introduction of a human capital or skills development fourth pillar of NICIS.

There is, in fact, a dual challenge: many mid-career researchers are becoming disempowered as these developments have overtaken them and the rising cohort of new researchers need to be empowered. Data Science is a composite of skills drawn from existing disciplines, and the term data scientist is a ubiquitous term which embraces analytic data scientists, data engineers and managers, data librarians and more. For example, e-Agriculture is increasing in importance, and data science has revolutionised marketing. The qualification has been designed as part of DST's National e-Science Postgraduate Teaching and Training Platform to address the development of human capital with the necessary knowledge and skills to conduct cutting edge research in the field of e-Science. 

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
Recognition of Prior Learning (RPL):
Where learners to the qualification do not meet the admission requirements as stated, the Recognition of Prior Learning policy of the institution will be used to consider the learners with an NQF Level 8 qualification for admission or to provide the learner with advanced standing within the qualification. The Master of Science in eScience will accept professionals who may be employed full-time in teaching or business positions. Prospective learners should have prior programming experience, mathematical and statistical training. RPL will be applied appropriately through rigorous administration, assessment and appeal processes. RPL trained practitioner conducts all RPL.

Entry Requirements:
The minimum entry requirement for this qualification is:
  • Bachelor of Science Honours in a related field, NQF Level 8.
    Or
  • Postgraduate Diploma in a related field, NQF Level 8. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory and elective modules at NQF Level 9 totalling 180 Credits.

    Compulsory Modules at Level 9, 120 Credits:
  • Research Methods and Capstone Project in Data Science, 15 Credits.
  • Data Privacy and Ethics, 15 Credits.
  • Research Report: Data Science, 90 Credits.

    Elective Modules at Level 9, 60 Credits (Choose four modules):
  • Adaptive Computation and Machine Learning, 15 Credits.
  • Large Scale Computing Systems and Scientific Programming, 15 Credits.
  • Statistical Foundations of Data Science, 15 Credits.
  • Special Topics in Data Science, 15 Credits.
  • Data Visualisation and Exploration, 15 Credits.
  • Large Scale Optimisation for Data Science, 15 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1. Apply advanced knowledge and skills of integrated e-Science disciplines to engage with academic debate and create solutions to workplace problems.
    2. Competently and independently use e-Science equipment to formulate, solve and analyse complex problems in e-Science creatively and efficiently.
    3. Communicate data science information effectively and logically to a wide variety of audience.
    4. Conduct advanced and applied research in e-Science under minimal supervision.
    5. Write and interpret research reports in e-Science. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcome 1:
  • A comprehensive and systematic knowledge base of the relevant concepts and theories of the broad area of e-Science disciplines, as well as specialisation in one specific area of the field, is demonstrated.
  • Translate knowledge and skills acquired to solve specific problems in the workplace.
  • Apply practical methods and techniques to real-life problems.
  • Integrate all concepts and theory to develop sustainable functional and strategic recommendations based on the area of specialisation.

    Associated Assessment Criteria for Exit Level Outcome 2:
  • Analyse and report on advanced Data Science problems.
  • Identify suitable instruments and technology applicable to solve specific real-life problems in Data Science creatively.
  • Apply Data Science techniques independently or as a team leader.
  • Critique one's work and provide alternative approaches to solving particular problems.

    Associated Assessment Criteria for Exit Level Outcome 3:
  • Analyse and present data to appropriately suit different areas in the real-world problems.
  • Adequately use relevant scientific formats to solve problems at hand.
  • Interpret Data Science outputs and results and prepare reports for different stakeholders depending on their area of expertise.

    Associated Assessment Criteria for Exit Level Outcome 4:
  • Identify a research problem and contextualise it appropriately.
  • Critique practical methods and techniques applicable to a particular field of research in Data Science.
  • Independently apply advanced methods to solve real-life problems in Data Science.
  • Independently assess and identify suitable methods and software applicable to specific areas of research in Data Science.
  • Engage in ethical debates regarding e-Science and practice within an ethical and moral context.

    Associated Assessment Criteria for Exit Level Outcome 5:
  • Translate research ideas and deductions in a clear and compelling manner.
  • Discern between relevant and irrelevant results to prepare a research report for peer review.
  • Write a scientific scholastic document using appropriate terminology, formatting and referencing skills.
  • Present sensible research reports in Data Science, from which others can learn.

    Integrated Assessment:
    Assessment will take the form of continuous formative assessments as well as summative assessments in all components of the qualification. Integration of assessment occurs across all learning outcomes. The criteria used to determine learner progress are explicit, and assessment tasks match to the learning outcomes. Assessment opportunities will be created for learners to show that they can demonstrate the achievement of several learning outcomes within a single assessment task. As per the institution assessment policy for learning, assessment practices vary, providing learners with opportunities to succeed.

    Assessments include written tests, assignments and examinations, practical assignments that assess the applied theories learned in lectures, essays, group work and presentations. Learners will complete a capstone project which will provide a transparent integration of theory, practical, research and critical enquiry. The research component of the qualification also presents an opportunity for integrated assessment across all outcomes. 
    		•

    INTERNATIONAL COMPARABILITY 
    This qualification is comparable with international qualifications presented at the below institutions:

    Country: United Kingdom.
    Institution: Queen Mary University in London.
    Qualification Title: Master of Science in Big Data.
    The Master of Science in Big Data is a one year taught qualification that includes a substantive industry-led project and requires learners to complete course work modules. Learners of the Master of Science in Data Science can explain the core concepts, techniques and tools needed for large-scale data analysis through lectures, laboratory sessions and tutorials. The learners will put these elements to practice through the execution of use cases extracted from real domains.

    The qualification purpose is to develop data scientists who are highly skilled professional. The learners can combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services based on mining the knowledge behind the data. The structure of the Queen Mary University qualification is similar to this qualification in that learners take three core subjects in applied statistics, Big data processing and data mining. Learners may then select from a range of modules of which machine learning for visual data analytics, data analytics overlap. Although Queen Mary University has a significant focus on industry application, the qualifications have similar purposes and outcomes. They expect to expand on the cadre of professionals with this highly specialised expertise.

    Country: United States of America.
    Institution: The George Washington University:
    Qualification Title: Master of Science in Big Data.
    Admission to the qualification requires learners of a Bachelor qualification to have Mathematics, Statistical and some computer competencies and compares to the entry requirements of the proposed qualification. The qualification purpose aims to produce learners for the future in an emerging field that aims to extract actionable insights from vast arrays of information. The qualification aims to provide professionals that meet the challenges of being able to understand data and contribute essential ideas that will change the way we live, work and communicate.

    The qualification is a taught Masters qualification with introductory data science and data mining qualifications, a data warehousing qualification and a Capstone Project. Learners may then electives based on their interest which include Machine learning Mathematics modules, high-performance computing modules, data science applied research, Topics in Data Sciences and visualisation of complex data. There is distinct overlap with this qualification, as well as a dichotomy in those modules such as Geographical Information Systems included in the George Washington University qualification. 

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

    Horizontal Articulation:
  • Master of Science in Computer Science, NQF Level 9.
  • Master of Science in Information Technology, NQF Level 9.

    Vertical Articulation:
  • Doctor of Science in Computer Science, NQF Level 10.
  • Doctoral of Philosophy, 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.
     
    1. University of Limpopo 


    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.