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SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Postgraduate Diploma in Data Analytics 
SAQA QUAL ID QUALIFICATION TITLE
117788  Postgraduate Diploma in Data Analytics 
ORIGINATOR
The Independent Institute of Education (Pty) Ltd 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
CHE - Council on Higher Education  HEQSF - Higher Education Qualifications Sub-framework 
QUALIFICATION TYPE FIELD SUBFIELD
Postgraduate Diploma  Field 10 - Physical, Mathematical, Computer and Life Sciences  Information Technology and Computer Sciences 
ABET BAND MINIMUM CREDITS PRE-2009 NQF LEVEL NQF LEVEL QUAL CLASS
Undefined  120  Not Applicable  NQF Level 08  Regular-Provider-ELOAC 
REGISTRATION STATUS SAQA DECISION NUMBER REGISTRATION START DATE REGISTRATION END DATE
Reregistered  EXCO 0821/24  2020-11-20  2027-06-30 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2028-06-30   2031-06-30  

In all of the tables in this document, both the pre-2009 NQF Level and the NQF Level is shown. In the text (purpose statements, qualification rules, etc), any references to NQF Levels are to the pre-2009 levels unless specifically stated otherwise.  

This qualification does not replace any other qualification and is not replaced by any other qualification. 

PURPOSE AND RATIONALE OF THE QUALIFICATION 
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. 

LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
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. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    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. 
    		•

    EXIT LEVEL OUTCOMES 
    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 
    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. 
    		•

    INTERNATIONAL COMPARABILITY 
    Country: New Zealand.
    Institution: The University of Canterbury.
    Qualification: Postgraduate Diploma in Applied Data Science.

    The Postgraduate Diploma in Applied Data Science has a similar design as the Postgraduate Diploma in Data Analytics. The similarity can be found in modules such as Introduction to Data Science, Statistics, Computer Programming, Data Management, Advanced-Data Science. The qualification structure is further divided into Group A: Advanced Data Science Competencies and Group B Domain-specific competencies. In group A they offer courses like Data Science, which is similar to the institution's Data Science module. The remaining courses in group B are relevant and/or equivalent level course in fields like Computer Science, Data Science, Digital Humanities.

    Country: New Zealand.
    Institution: Manukau Institute of Technology.
    Qualification: Graduate Diploma in Data Analytics.
    This qualification is comparable to the IIE's Postgraduate Diploma as it embraces the inclusion of components such as Big Data Analysis, Business Statistics for Decision Modelling, Data Analytics and Intelligence. The similarity can also be noted in the integration of Data Analytics discourses; Advanced Data Analytics; Management of ICT and an Industry Project.

    Country: Canada.
    Institution: University of Waterloo.
    Qualification: Graduate Diploma in Data Analytics.
    The structure of the qualification has specific modules such as Big Data Analytics, Statistical Methods for Data Analytics and Operational Analytics, which is comparable to the institution's Postgraduate Diploma in Data Analytics. The common purpose between the Graduate Diploma in Data Analytics and the institution's Postgraduate Diploma in Data Analytics is that both qualifications equip learners with the tools to analyse data to increase an organisation's efficiency and profitability. 

    ARTICULATION OPTIONS 
    This qualification allows possibilities for both vertical and horizontal articulation.

    Horizontal Articulation:
  • Bachelor of Computer and Information Sciences Honours, NQF Level 8.

    Vertical Articulation:
  • Master of Philosophy in Computer and Information Sciences, NQF Level 9. 
    		•

    MODERATION OPTIONS 
    N/A 

    CRITERIA FOR THE REGISTRATION OF ASSESSORS 
    N/A 

    NOTES 
    N/A 

    LEARNING PROGRAMMES RECORDED AGAINST THIS QUALIFICATION: 
     
    NONE 


    PROVIDERS CURRENTLY ACCREDITED TO OFFER THIS QUALIFICATION: 
    This information shows the current accreditations (i.e. those not past their accreditation end dates), and is the most complete record available to SAQA as of today. Some Primary or Delegated Quality Assurance Functionaries have a lag in their recording systems for provider accreditation, in turn leading to a lag in notifying SAQA of all the providers that they have accredited to offer qualifications and unit standards, as well as any extensions to accreditation end dates. The relevant Primary or Delegated Quality Assurance Functionary should be notified if a record appears to be missing from here.
     
    1. The Independent Institute of Education (Pty) Ltd 


    All qualifications and part qualifications registered on the National Qualifications Framework are public property. Thus the only payment that can be made for them is for service and reproduction. It is illegal to sell this material for profit. If the material is reproduced or quoted, the South African Qualifications Authority (SAQA) should be acknowledged as the source.