Bachelor of Science Honours in Data Science

Purpose

The Bachelor of Science Honours in Data Science is the first Postgraduate Degree qualification that follows on a Data Science curriculum at an undergraduate level. It will provide students with a knowledge base, theory and practice of Data Science with specific reference to data collection, analytics and visualisation coupled with respective algorithm development.

The purpose of this qualification is to

• Produce qualifying learners who have a systematic and coherent body of knowledge and an understanding of underlying concepts and principles of Data Science; the ability to access and evaluate scientific information including knowing how scientific knowledge is created; a high level of cognitive and other generic skills including problem-solving, written and spoken communication and computer literacy; and competence in applying knowledge through basic research methods and practice.
• Provide every qualifying learner with a sufficient depth of knowledge and skills that give opportunities for continued personal intellectual growth in a Postgraduate study, for gainful economic activity in a range of careers associated to data science, and for rewarding and constructive contributions to society.
• Provide society with qualifying learners who demonstrate initiative and responsibility, who are professional and ethical in their roles within the economy and society, and who are able to be intellectual leaders within their society.
• Produce qualifying learners in order to increase, widen and transform the leadership base in South Africa, both for innovation and science-based economic and research development, and for the education of future generations of scientists, technologists, engineers and other professional people.

Rationale

The challenges of digital transformation can also be attributed to lack of skills and knowledge to efficiently transform data science and its technologies. A recent report on a survey by Gartner indicate that 59% Information Technology (IT) professionals believed that their organisations were not ready to meet the challenges of digital revolution due to shortage of technical skills. The introduction of a Bachelor of Science Honours Degree in Data Science will address a critical skills shortage in the country and will provide access to learners to an advanced area of study in a critical contemporary discipline. The programme has been carefully designed to specifically focus on computing structures that support large scale computing challenges. Qualifying learners will contribute immensely in solving analytically complex problems in real life settings such as in industry, Government and other forms of organisations at national and international level.

Recognition of Prior Learning (RPL)

For the purpose of admission into the programme applying admission criteria other than described above, candidates could be assessed as per Institutional RPL Policies and Procedures.

Entry Requirements

The minimum requirements for admission into the Bachelor of Science Honours in Data Science are

• A Bachelor of Science Degree in Data Science or an equivalent qualification.

This qualification comprises of compulsory and elective modules at Level 8 totalling 120 Credits.

Electives Modules Level 8, (Select one-12 Credits)

• Multidimensional Signal Processing, 12 Credits.
• Special Topics in Data Science, 12 Credits.

Compulsory Modules Level 8, 108 Credits

• Research Project: Data Science, 36 Credits.
• High Performance Computing, 12 Credits.
• Computer Systems for Big Data, 12 Credits.
• Large Scale Optimisation, 12 Credits.
• Advanced Machine Learning, 12 Credits.
• Data Exploration and Visualisation, 12 Credits.
• Data Security and Cryptographic, 12 Credits.

1. Demonstrate specialised knowledge within the Data Science discipline.
2. Research literature and other available resources to gain a better insight in the topics at hand.
3. Make decisions on ethical values and practices within the Data Science discipline.
4. Communicate effectively regarding topics within the Data Science discipline.
5. Demonstrate key scholarly skills through logical thinking and reasoning.
6. Make an informed decision on the types of methods and techniques available to execute, analyse and report on a research topic within the Data Science discipline.
7. Develop competence in reading and understanding, and critically appraising text/literature in the context they are written.
8. Demonstrate competence in analytical thinking through the synthesis of information from relevant sources and applying them to real problems.
9. Take responsibility for actions and decisions made.

Associated Assessment Criteria for Exit Level Outcome 1

• Use terminology, concepts, principles and theories in written and/or oral communication are correctly.
• Critically appraise relationship among concepts and principles of Data Science discipline are critically.
• Apply knowledge effectively to propose solutions to problems in the field of interest are effectively within the Data Science discipline.

Associated Assessment Criteria Exit Level Outcome 2

• Reflect in explicit recognition of the diversity, complexity and multi-dimensionality of a context and how that affects the particular work being undertaken.
• Demonstrate through the provision of relevant information pertaining to the strengths, weaknesses and opportunities of the context for addressing specific problems.
• Identify relevant role players and resources that will contribute to resolution of specific problems.
• Describe all relevant factors pertaining to the context and performance(s) in these contexts and how they affect the particular work being undertaken.
• Identify critical factors impacting on practical problems to be investigated from the perspective of the discipline.

Associated Assessment Criteria for Exit Level Outcome 3

• Know the ethical implications of various kinds of research and be able to act accordingly.

Associated Assessment Criteria Exit Level Outcome 4

• Apply reasoning skills to by expressing own opinions clearly and coherently, justifying a position and presenting it logically, systematically using properly substantiated arguments.
• Show an awareness of audience, and capability in using different modes of communication (oral and written) and discipline-specific conventions, and utilisation of different techniques and strategies for communicating results.

Associated Assessment Criteria Exit Level Outcome 5

• Demonstrate logical thinking (including identification of flawed reasoning in a text).
• Demonstrate inductive and deductive thinking skills.
• Demonstrate thinking and reasoning (self-reflexivity is demonstrated at the appropriate level).

Associated Assessment Criteria Exit Level Outcome 6

• Identify a research problem.
• Consult appropriate literature.
• Select methodologies that are appropriate to addressing the research problem.
• Undertake data collection, presentation, analysis and interpretation of data.
• Produce a written report of the research project, including inter alia the findings, conclusions and recommendations.

Associated Assessment Criteria Exit Level Outcome 7

• Identify the central theme (s) of the essay.
• Extract and explaining the competing arguments.
• Give a knowledgeable perspective to the debate.
• Derive a conclusion that fills the gap.

Associated Assessment Criteria Exit Level Outcome 8

• Structure the essay by showing understanding of the question and indicating the direction of the answer through laying out the subsections of the essay and their linkages with the overall theme.
• Operationalise the topic by stating clearly the context in which its basic concepts and assumptions are to be evaluated in the essay.
• Debate the subject matter by developing logically coherent arguments as well as citing and discussing illustrative examples.
• Draw a conclusion that is consistent with the arguments raised in the essay and suggesting possible outcomes of alternative initiatives at addressing the subject matter.
• Present the essay according to appropriate and/or recommended word processing and referencing protocol.

Integrated Assessment

The purpose of this assessment is to establish student learner achievement of the required knowledge at this level and also the ability to use and apply practical skills learnt from the programme. This will mainly be done through formulation and carrying out a research project, reporting on it and critically reviewing current literature in the Data Science spectrum.

Quality assessment is central to credible certification and recognition of student achievement. SPU (Sol Plaatje University) the Institution will ensure credibility in assessment through the application of clear and rigorous procedures and practices, in keeping with the principles of fairness, validity, reliability and practicability.

Integrated Assessment is used extensively across the qualification. Self-and peer assessment takes place in various ways in the face to face context, including through classroom activities, assignments, and written work (formative assessments).

Summative Assessments are integrated into the learning in each of the constituent modules of the programme.

Assessment methods will normally include formal examinations assessment (i.e. written examinations), written reports, the creation and presentation of data science artefacts (such as data sets, models, and computer program source code) and oral presentations.