Master of Science in Data Science

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.

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.

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.

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.

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.