Master of Science in e-Science
Purpose:
Source: SAQA official qualification record. Yiba Verified does not own the underlying qualification data shown on this page.
Qualification type
Master's Degree
Credits
180
Sub-framework
HEQSF - Higher Education Qualifications Sub-framework
Providers listed
0
Qualification snapshot
Official qualification identity fields captured from the qualification record.
Originator
Sol Plaatje University
Quality assurance functionary
-
Field
Field 10 - Physical, Mathematical, Computer and Life Sciences
Subfield
Information Technology and Computer Sciences
Qual class
Regular-Provider-ELOAC
Recognise previous learning
Y
Important dates
These dates are carried directly from the qualification record.
Registration start
2025-07-10
Registration end
2028-07-10
Last date for enrolment
2029-07-10
Last date for achievement
2032-07-10
Purpose and entry context
Official SAQA text formatted for easier reading.
Purpose and rationale
Purpose
The Master of Science in e-Science aims to prepare learners in the disciplines of Computer Science, Statistics, Mathematics and Applied Mathematics, as well as Data Science, to gain an interdisciplinary perspective on the emerging fields of e-Science, for the growing areas of e-Science affected by advances in cyber-infrastructure (computers/networks; data analytics/visualisation; data collection and storage, etc.). The qualification will create opportunities for learners to gain an interdisciplinary perspective on the emerging fields of e-Science. This will contribute to national and international priorities such as improving service delivery and developing a competitive knowledge economy, and it will create opportunities for qualifying learners as professional researchers in academia and the public and private sectors. Upon completion of this qualification, qualifying learners will be able to:
- Apply advanced e-Science knowledge and skills in the workplace.
- Competently and independently use e-Science equipment to formulate, solve and analyze problems in e-Science.
- Identify suitable instruments and technology applicable to specific real-life problems in Data Science.
- Communicate data science information effectively and logically to a wide variety of audience.
- Adequately use relevant scientific formats to problems at hand.
- Conduct advanced and applied research in e-Science under minimal supervision.
- Write and interpret research reports in e-Science.
Rationale
This qualification is designed to train postgraduate learners in computational, mathematical, and statistical methods to solve data-driven problems. The qualification will create opportunities for learners in Data Science, Computer Science, Statistics, Physics, Electrical Engineering, or related fields to gain an interdisciplinary perspective on the emerging fields of e-Science. The qualification aims to prepare learners in these disciplines for the growing areas of e-Science affected by advances in cyberinfrastructure (computers/networks; data analytics/visualisation; data collection and storage, etc.). This will contribute to national and international priorities such as improving service delivery and developing a competitive knowledge economy, and it will also create opportunities for qualifying learners as professional researchers in the public and private sectors.
The recent past decade has just experienced the beginning of a data explosion for which more knowledge and skills are going to be required. This can be attributed to innovative technologies in areas of computer hardware and software that makes it possible to capture a variety of data from various sources and geographical locations. As a result, this has led to a demand for data-savvy professionals in the industry, public agencies, and non-profit organisations. However, there has been an insufficient supply of these professionals globally. For instance, in 2017, Teradata estimated a shortage of data scientists to approximately one million in Asia-Pacific This skills shortage has a direct negative impact on economies as observed by the APEC Human Resource Development Working Group.
Globally, academic institutions have been responding to this call by introducing new qualification offerings in data science. Data science has emerged as an interdisciplinary field involving social sciences, statistics, computer science, mathematics, and many other data related disciplines. Southern Africa equally observed a data skills gap within the region that needs to be addressed if businesses were to appropriately make sense of their data and noted that the demand for data scientists is 60% more than the current supply.
The main objective of this qualification is to develop human capital with essential skills and knowledge to conduct research in the field of e-Science applicable to data science projects in areas such as the South African Research Infrastructure Roadmap (SARIR), the Square Kilometre Array (SKA), industry and other data-oriented organisations. This objective follows an initiative from DST that established a multi-institutional NEPTTP under the National Integrated Cyber-Infrastructure System (NICIS) programme. The Platform is intended to develop suitable qualifications, curricula, and pedagogic interventions to advance the training of postgraduate learners in the rapidly developing cross-disciplinary fields involved in e-Science. It is a multi-institutional programme whose primary purpose is to develop and implement national Masters qualifications in the field of e-Science. It is currently being operationalised by a Consortium of six South African universities and the CSIR Meraka Institute (MI) was appointed as its agency to play the role of oversight towards the implementation and operationalisation of this qualification.
Entry requirements and RPL
Recognition of Prior Learning (RPL)
This qualification may be achieved in part or in whole through the Recognition of Prior Learning, which includes formal, informal, and non-formal learning and work experience. The Recognition of Prior Learning (RPL) assessment process involves the identification, mediation, assessment and acknowledgement of knowledge and skills obtained through informal and non-formal learning. RPL is applied in terms of policy and criteria of the institution. The necessary documentary evidence will have to be provided and a formal RPL process will be followed. The institution will apply in this qualification the RPL for both access and credits in line with the National Policy and Criteria for the Implementation of RPL (Amended in March 2019).
RPL for Access
Learners who do not meet the minimum entry requirements of the required qualification may be considered for RPL. There are two options:
- Advanced Standing, in which case the minimum entry requirements are waived by the admitting institution based on evidence of prior learning, work experience or any other relevant circumstances that may apply to an individual learner. No portfolio is required.
OR
- Applicants may provide evidence in the form of a portfolio that demonstrates that the applicant has acquired sufficient relevant knowledge, skills, and competencies to be able to reasonably meet the expectations for learning demanded by the qualification for which they are seeking access.
- In instances where RPL is applied for the purposes of access, no credits will be awarded for any previous learning. However, the candidate may choose the option of being assessed for credit.
RPL for credits
Learners who do meet the necessary entry requirements for admission to a qualification may be awarded some or all the credits towards the qualification. There are two possibilities:
- Learners may apply for RPL to be exempted from a module or some modules by providing sufficient evidence in the form of a portfolio that demonstrates that a level of competency, equivalent to the learning outcomes of the module or modules, has been achieved. Credits will be awarded for such modules.
OR
- Learners may apply for RPL to be awarded all the credits required for the qualification. Sufficient evidence must be provided that demonstrates a level of competency equivalent to all the learning outcomes of the qualification.
Entry Requirements
The minimum entry requirement for this qualification is
- Bachelor of Science Honours in the related field, NQF Level 8.
Or
- Postgraduate Diploma in a related field, NQF Level 8.
Replacement note
This qualification does not replace any other qualification and is not replaced by any other qualification.
Structure and assessment
Qualification rules, exit outcomes, and assessment criteria from the SAQA record.
Qualification rules
This qualification consists of the following compulsory and elective modules at National Qualifications Framework Level 9 totalling 180 Credits.
Compulsory Modules, Level 9, 120 Credits
- Mini-Dissertation/Research Report (Data Science), 90 Credits.
- Research Methods and Capstone Project in Data Science, 15 Credits.
- Data Privacy and Ethics, 15 Credits.
Electives Modules, Level 9, 60 Credits (Select four modules)
- Adaptive Computation and Machine Learning, 15 Credits.
- Data Visualisation and Exploration, 15 Credits.
- Large Scale Computing Systems and Scientific Programming, 15 Credits.
- Large Scale Optimisation for Data Science, 15 Credits.
- Mathematical Foundations of Data Science, 15 Credits.
- Special Topics in Data Science, 15 Credits.
- Statistical Foundations of Data Science, 15 Credits.
Exit level outcomes
- Apply advanced e-Science knowledge and skills in the workplace.
- Competently and independently use e-Science equipment to formulate, solve and analyse problems in e-Science.
- Communicate data science information effectively and logically to a wide variety of audience.
- Conduct advanced and applied research in e-Science under minimal supervision.
- Write and interpret research reports in e-Science.
Associated assessment criteria
Associated Assessment Criteria for Exit Level Outcome 1
- Translate knowledge and skills acquired to solve specific problems in the workplace
- Apply practical methods and techniques to real life problems.
Associated Assessment Criteria for Exit Level Outcome 2
- Analyse and report on advanced Data Science problems
- Identify suitable instruments and technology applicable to specific real-life problems in Data Science
- Apply Data Science techniques independently or as a team leader.
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 problems at hand
- Interpret Data Science outputs and results and prepare reports
Associated Assessment Criteria for Exit Level Outcome 4
- Independently apply advanced methods to solve real-life problems in Data Science
- Critique practical methods and techniques that apply to a particular field of research in Data Science.
- Present sensible research reports in Data Science, from which others can learn
Associated Assessment Criteria for Exit Level Outcome 5
- Translate research ideas and deductions in a clear and effective manner
- Independently assess and identify suitable methods and software applicable to specific areas of research in Data Science
- Identify and solve problems in the workplace.
Progression and comparability
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
- Doctor of Philosophy in Computer Science/Statistics/Applied Mathematics, NQF Level 10.
- Doctor of Science, NQF Level 10.
International comparability
Several institutions globally are now offering Master of Science in data science qualifications. The international comparison was conducted with the following international institutions. This comparison shows that the Consortium is offering an internationally comparable curriculum.
Country: United Kingdom (UK)
Institution: University of Oxford
Qualification Title: Master of Science in Social Data Science.
Entry requirements
As a minimum, applicants should hold or be predicted to achieve the equivalent of the following UK qualifications
- A first-class undergraduate degree with honours in any subject.
- In exceptional circumstances, applicants with a distinguished record of workplace experience or other relevant achievements may be accepted with lower grades at undergraduate level.
- Applicants from industry to include at least one reference from an academic or someone in academic-related field.
- For applicants with a degree from the United States of America, the minimum GPA sought is 3.7 out of 4.0.
- Applicants are normally expected to demonstrate quantitative aptitude or experience in introductory calculus and matrix algebra, equivalent to, for example:
- A-levels mathematics
- Mathematical Studies SL from the International Baccalaureate Diploma Programme.
Or
- Advanced Placement (AP) Calculus AB.
Purpose/Rationale
The qualification provides the social and technical expertise needed to collect, critique, and analyse unstructured heterogeneous data about human behaviour, thereby informing the understanding of the social world.
Social data generated digitally (from, for example, social media, communications platforms, Internet of Things (IoT) devices, sensors/wearables, and mobile phones) offer a way to accumulate new large-scale data, in addition to existing data that have been converted to digital formats. These data can be put to work helping us understand big issues of crucial interest to the social sciences, industry, and policymakers including social, economic, and political behaviour, interpersonal relationships, market design, group formation, identity, the international movement, ethics, and responsible ways to enhance the social value of data, and many other topics.
The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real-world data; critiquing inequalities inherent in the data or manifested through analyses of this data and developing the tools and techniques to make meaningful claims about the social world, through explanation, prediction, and experimentation. In this way, social data science offers a data science where the data relates to individual and social behaviour and social science with generation and analysis of real-time transactional data at its centre.
Due to the intensive nature of the taught portion of this course, there is no part-time option available. However, learners continuing to doctoral study have the option of taking a part-time DPhil. Employers recognise the value of a degree from the University of Oxford, and graduates from the existing qualifications have secured excellent positions in industry, government, NGOs, or have gone on to pursue doctoral studies
Assessment
Learners will take a combination of core and optional papers and produce a dissertation of up to 15,000 words with the support of a thesis supervisor. The thesis provides learners the opportunity to apply the methods and approaches they have covered in the other parts of the course and carry out a substantive piece of academic research.
Country: United Kingdom.
University: Queen Mary University of London.
Qualification Title: Master of Science (MSc) in Big Data Science.
Similarities
Duration of qualification: MSc (One year Full-time/Two years Part-time).
Entry qualifications and modules of the qualification
- An upper second-class degree is typically required, usually in electronic engineering, computer science, maths, or a related discipline. Learners with a good lower second-class degree may be considered on an individual basis.
- Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.
Exit Level Outcomes
- Gain knowledge in data science.
- Understand computational and probabilistic principles underpinning modern machine learning and data mining algorithms.
- Apply data science to analyse scientific and business data Implement machine learning solutions through high-performance computing platforms.
- Discover new knowledge through experimentation and investigative methods.
Differences
Differences in module structures could be attributed to the difference in drivers of the two qualifications. However, there is apparent convergence in terms of applications and core modules of the qualifications. Queen Mary's qualification is quite intensive, requiring learners with a good background in terms of computing, statistical and mathematical modelling capabilities. Learners are mainly not so prepared for that because of the Honours qualifications which do not necessarily prepare for them to do data science.
Country: Finland.
University: University of Helsinki.
Qualification Title: MSc in Data Science Versus.
Comparison
Duration of qualification: 2 years for both qualifications.
Number of credits: 120 for Helsinki and 180 for this institution.
Exit Level Outcomes
Qualifying learners will be able to
- Gain knowledge in data science.
- Understand computational and probabilistic principles underpinning modern machine learning and data mining algorithms.
- Apply data science to analyse scientific and business data Implement machine learning solutions through high-performance e-computing platforms.
- Discover new knowledge through experimentation and investigative.
- Apply knowledge gained in Business and Scientific research into life processes are critical areas of applications.
Qualification structure
The qualification comprises of the following compulsory modules which are similar to the South African qualification
- Distributed systems.
- Programming.
- Projects (cape stone project).
- Research project.
Country: United States of America
Institution: University of Washington DC
Qualification Title: Master of Science in Data Science
Credits: 45 credits, consisting of eight core courses (40 credits) and a two-quarter capstone project (5 credits).
Similarities
Duration: 1.5-year, full-time
Purpose/Rationale
The digital revolution brings an explosion of data with significant value for businesses, science, and society. As data becomes larger and more complex, extracting useful quantitative insights becomes more challenging.
The aim of the qualification is to train future leaders in the field with a deeper understanding of the underlying methods, along with the ability to develop techniques for new and/or non-standard problems.
The qualification prepares graduates to design and build data-driven systems in the private, public and research sectors. The curriculum guides learners from modelling and theory to computational practice and cutting-edge tools, teaching skills that are in growing global demand.
The qualification offers a quick and clear pathway to earn your master's degree and accelerate your career in data science. The full-time qualification is an excellent fit for recent college graduates who want to begin their careers in data science. It is also a strong option for experienced professionals who want to transition careers or advance their careers by fully immersing themselves in a data science program. The full-time learners come to us from across the country and around the world with the goal of accelerating their data science careers.
The learners will gain a solid knowledge of Statistics, Machine Learning theory and methods such as Reinforcement Learning and Deep Learning, Optimization and Computing. The qualification also prepares learners to critically assess the validity and performance of existing methods, and to address potential limitations by developing new ones. Learners will learn how to apply classroom examples using real data and answering concrete questions from the perspectives of different sectors. Through an independent master's project or an industrial practicum conducted with local businesses or research teams, learners can solve actual analytics problems hands-on.
Qualification structure
The Master of Science in Data Science curriculum is designed to provide the breadth and depth of knowledge needed for a successful career in data science. It emphasizes practical proficiency in applying the relevant skills through modules in:
- Statistical modelling.
- Data management.
- Machine learning.
- Data visualization.
- Software engineering.
- Research design.
- Data ethics.
- User experience.
Depending on the course, learners can expect an emphasis on Python and R programming and some assignments in Java. Many of the courses will emphasize team-based data analysis and engineering work and will involve working in small groups to complete one or more guided practicum projects per quarter. The final two quarters include a capstone project where learners get to solve a real-world challenge facing an external organization.
Conclusion
Best practices have been identified from leading institutions locally and abroad, ensuring that the curriculum and content are relevant and addresses the needs of both industry and prospective learners. The South African qualification is comparable to the above international qualifications in terms of content, purpose, rationale and learning outcomes.
Providers currently listed
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