Postgraduate Diploma in Data Analytics

Purpose

The purpose of the Postgraduate Diploma in Data Analytics is to develop fundamental knowledge in Big Data, Data Visualisation, Artificial Intelligence (AI), Machine Learning (ML) and Statistics. Learners who complete the qualification will have the contextual knowledge and practical ability to work as a junior data analyst under the leadership of Data Scientists/Analysts in a Data Science team. The qualification targets learners with modules in computer science, information systems, software engineering and application development. On completion of the qualification, learners would have attained the theoretical and technical skills in Data Analytics to inform business decisions and articulate into an appropriate Master's Degree.

Rationale

There is a need to utilise Information and Communication Technology (ICT) to solve socio-economic challenges in South Africa, by employing innovative ideas, building skills in Big Data Analytics, and producing highly skilled and knowledgeable workers in the Data Analytics field. Similarly, there are growing volumes of big data in South Africa (both structured and unstructured) that needs to be analysed to make it useful, trustworthy, valuable and secured (Roy, 2019). Additionally, there is a need for managers and business analysts who can understand and interpret the analysis outputs to make effective business decisions (Jain, 2018).

Data Analytics and Data Sciences are emerging fields offering exciting opportunities for highly skilled qualified learners with specialised knowledge. Most Data Scientists working in the industry (particularly in South Africa) are working at very senior levels, having migrated from related fields such as Business Intelligence or Data Analytics (Jain, 2018). They are also moving from specialised areas such as actuarial science, applied mathematics, and statistics, and in general, have many years of work experience coupled with Masters or Doctoral degrees. As the adoption of Data Analytics and Data Science proliferates, there is a growing use of data science teams lead by senior data scientists, to counter the lack of skills. The Postgraduate Diploma in Data Analytics will produce qualified learners who are well equipped to function in such teams.

Learners from the Bachelor of Computer and Information Sciences in Application Development have a meaningful software development acumen and an in-depth understanding of application development and programming. There is a need to strengthen learners' knowledge and develop new skills in software development to enable specialisation in the emerging field of Big Data Analytics and articulation into a postgraduate qualification. Therefore, the Postgraduate Diploma in Data Analytics takes into consideration the identified gaps in the industry including the learning and development needs, by providing a coherent and structured qualification that will enable learners to be competent in Data Analytics, Programming, Applied Research, Artificial Intelligence (AI), Machine Learning (ML) and Statistics.

Recognition of Prior Learning (RPL)

The Recognition of Prior Learning (RPL) procedure is governed by institutional policies.

The learning assumed to be in place for the qualification is assessed against Exit Level Outcomes equivalent to the formal learning required for admission. This would include an evaluation of the content as well as the learner's cognitive and technical competence. Learners are provided with the qualification specific instrument, Credit Accumulation and Transfer, Recognition of Prior Learning and Qualification Completion Policy and supporting documentation. Learners are required to provide both an argument and a portfolio of evidence to demonstrate how they achieved the learning assumed to be in place.

The Credit Accumulation and Transfer, Recognition of Prior Learning and Qualification Completion Policy distinguish between RPL for access, which provides an alternative access route into a qualification and RPL for credit, which provides for the awarding of credits for, or towards a qualification or part qualification registered on the NQF.

The RPL processes that are followed to recognise and assess prior knowledge and skills gained through informal, non-formal or experiential learning are as follows:

RPL applications are evaluated against the entry requirements to qualification and the process thus entails the following. Expert handling the application.

• Presentation by the learner of evidence of the prior learning relative to the expectations.
• Assessment of learner's presentation of skills, knowledge and experience by the assessment committee.
• Documentation of the proposed outcome by the responsible academic.
• Acknowledging, through admission to a course of study, the learner's skills, knowledge and experience built up through formal, informal and non-formal learning that occurred in the past.

Entry Requirements

The minimum entry requirement for this qualification is

• An appropriate Bachelor's Degree at NQF Level 7.

Or

• An appropriate Advanced Diploma at NQF Level 7.

This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 120 Credits.

Compulsory Modules, Level 8, 120 Credits

• Data Analytics 1, 15 Credits.
• Statistical Mathematical Analysis, 15 Credits.
• Programming for Data Analytics 1, 15 Credits.
• Research Proposal, 15 Credits.
• Data Science, 15 Credits.
• Data Analytics 2, 15 Credits.
• Programming for Data Analytics 2, 15 Credits.
• Research Project, 15 Credits.

1. Identify, manage, analyse and interpret data through the application of appropriate techniques to inform business decisions.
2. Design, develop, maintain and implement data collection and management approaches to support data analysis.
3. Identify, analyse and interpret trends or patterns in complex data sets and present information using multiple visualisation techniques.

Associated Assessment Criteria for Exit Level Outcome 1

• Examine, sort, and manipulate raw data.
• Identify and analyse data ethically and objectively.
• Identify appropriate analytical techniques for given contexts or data.
• Use appropriate statistical models for data analysis.
• Identify data trends to support business decisions.

Associated Assessment Criteria for Exit Level Outcome 2

• Design experiments and other data collection methods.
• Implement appropriate data governance frameworks.
• Maintain useful, trustworthy, valuable and secured structured and unstructured data collections.

Associated Assessment Criteria for Exit Level Outcome 3

• Recommend specific approaches to support known business problem-solving.
• Produce relevant and insightful research reports.
• Identify patterns found through big data analytics, to use for targeted marketing, social development and business strategies.
• Analyse, in real-time, huge volumes of user behavioural data using machine learning.

Integrated Assessment

Formative Assessment

Learning and assessment are integrated. Continual formative assessment is required so that learners are given feedback on their progress in the achievement of learning outcomes. The scheme of work includes assignments, real-world briefs, tests and an integrated programme portfolio based on the learning material and learners are given feedback. The process is continuous and focuses on smaller sections of the work and limited numbers of outcomes.

Summative Assessment

Summative assessment is concerned with the judgement of the learning concerning the Exit Level Outcomes of the qualification. Such judgement must include integrated assessments which test the learner's ability to integrate the larger body of knowledge, skills and attitudes that are represented by the Exit Level Outcomes as a whole.

Examinations, projects, reports or equivalent assessments, such as a portfolio of evidence, assess a representative selection of the outcomes practised and assessed. The summative assessment also tests the learner's ability to manage and integrate a large body of knowledge to achieve the stated outcomes of a module. Integrated assessments will be designed to achieve.