Master of Science in eScience

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.

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.

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.

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 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.