SAQA 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.
SOUTH AFRICAN QUALIFICATIONS AUTHORITY 
REGISTERED QUALIFICATION: 

Postgraduate Diploma in Data Science 
SAQA QUAL ID QUALIFICATION TITLE
122148  Postgraduate Diploma in Data Science 
ORIGINATOR
Regenesys Management (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
Registered  EXCO 0922/24  2024-03-07  2027-03-07 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2028-03-07   2031-03-07  

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 Post Graduate Diploma in Data Science qualification is designed to develop data analysis and computational abilities in learners. The status of the business environment is being disrupted by technological developments that simplify the storage and processing of data for effective decision-making. There is a wide range of data used and generated in all walks of life. Hence, it is essential to understand and grasp the 3Vs of data, that is volume, variety, and velocity and data science techniques that enable the development of automated procedures for such incomprehensible data, including enhanced productivity, quality of work, and greater accuracy.

The primary goal of the qualification is to provide learners with a specialised grasp of data analytical skills using data science techniques in their respective domains and across a wide range of disciplines and industries.
The qualification aims to enhance the knowledge of prospective learners, give them an awareness of the data-driven world, and make them employable. This qualification is a multidisciplinary qualification which includes, learning how to use data in a business context and how massive data sets can be managed and analysed to make appropriate decisions.

Upon completion of the qualification, qualifying learners will be able to:
  • Understand and identify different components of the data ecosystem.
  • Build interactive dashboards to analyse data retrieved from various sources and extract business insights.
  • Explain Statistical, Probability and Linear Algebraic techniques for better understanding of the data.
  • Analyse and synthesize raw data to draw conclusions to make data-driven decisions using Python language-based tools.
  • Develop exploratory data analysis (EDA) mini project based on modules 1 to 4, using Python programming language.

    Rationale:
    Technology has become one of the means to manage business challenges, including maintaining growth, efficiency, and effectiveness. The institution recognises the importance of professional technology education and has taken the initiative to provide a Postgraduate Diploma in Data Science at the National Qualifications Framework (NQF) level 8.

    This qualification is designed to enable data scientists to manage complex data and provide solutions in the business environment globally. Having earned this credential, data scientists will be able to handle and analyse volumes of data utilising cutting-edge data science methods, including those that quickly transform and link information to artificial intelligence (AI).

    The qualification includes a foundation in the data ecosystem, mathematical and statistical techniques, and the use of a variety of tools. As learners progress through, they will learn how to develop end-to-end data science applications or solutions using several data analysis and data visualisation tools based on statistical techniques. Further, they will also develop predictive models using classical machine learning algorithms and neural network-based learning algorithms. These algorithms can be applied to both structured and unstructured data to solve various business problems.

    The qualification aims to assist South African youth in becoming employable across varied industries. Professionals will learn and gain advanced knowledge in implementing data analysis in business, mitigating ways within an ethical framework, and the capacity to choose the most suitable technique from which every industry will benefit as the paradigm shifts to include forward-thinking and flexibility. There is a strong demand for those with the ability to manage data and provide a company with a viable strategy for attaining its objectives. 
    		•

    LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
  • Learners who do not meet the minimum entrance requirements or the required qualification that is at the same NQF level as the qualification required for admission may be considered for admission through RPL.
  • To be considered for admission in the qualification based on RPL, applicants should provide evidence in the form of a portfolio that demonstrates that they have acquired the relevant knowledge, skills, and competencies through formal, non-formal and/or informal learning to cope with the qualification expectations.

    RPL for exemption of modules:
  • Learners may apply for RPL to be exempted from modules that form part of the qualification. For a learner to be exempted from a module, the learner needs to provide sufficient evidence in the form of a portfolio that demonstrates that competency was achieved for the learning outcomes that are equivalent to the learning outcomes of the module.

    RPL for credit:
  • Learners may also apply for RPL for credit for or towards the qualification, in which they must provide evidence in the form of a portfolio that demonstrates prior learning through formal, non-formal and/or informal learning to obtain credits towards the qualification.
  • Credit shall be appropriate to the context in which it is awarded and accepted.

    Entry Requirements:
    The minimum entry requirement for this qualification is:
  • Bachelor of Computer Science, NQF Level 7.
    Or
  • Bachelor of Science in Data Science, NQF Level 7.
    Or
  • Bachelor of Information Technology, NQF Level 7.
    Or
  • Advanced Diploma in Information Technology, NQF Level 7. 
    		•

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory modules at National Qualifications Framework Level 8 totalling 130 Credits.

    Compulsory Modules, NQF Level 8, 130 Credits.
  • The Data Ecosystem, 10 Credits.
  • Data storytelling using Power BI, 15 Credits.
  • Data Science with Python, 25 Credits
  • Statistics for Data Science, 10 Credits.
  • Mini Project (Module 1 - 4), 10 Credits.
  • Predictive modelling with machine learning, 25 Credits.
  • Deep Learning with artificial neural networks, 20 Credits.
  • Major Project, 20 Credits. 
    		•

    EXIT LEVEL OUTCOMES 
    Exit Level Outcomes
    1. Understand and identify different components of the data ecosystem.
    2. Build interactive dashboards to analyse data retrieved from various sources and extract business insights.
    3. Explain Statistical, Probability and Linear Algebraic techniques for a better understanding of the data.
    4. Analyse and synthesize raw data to draw conclusions to make data-driven decisions using Python language-based tools.
    5. Develop exploratory data analysis mini project based on modules 1 to 4, using Python programming language.
    6. Build, train and evaluate machine learning models for regression, classification, and clustering problems.
    7. Understand the structure and functioning of artificial neural network models for unstructured data like images and text.
    8. Develop end-to-end data science projects encompassing all stages of data science such as data collection, data analysis, model building, evaluation and deployment. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcomes 1.
  • Determine the broad nature of the data and explain the 5Vs of data and data values.
  • Discuss the dimensions of data and explain the data labelling concept.
  • Extract data based on the occurrence of events and identify input and output data.
  • Apply knowledge to analyse and visualize data.
  • Illustrate and interpret data into meaningful information and assess findings.

    Associated Assessment Criteria for Exit Level Outcomes 2.
  • Discuss features and components of Power BI and use them to connect to different data sources.
  • Explain different data types supported by Power BI.
  • Use Power BI Functions for mathematical and statistical calculations.
  • Select and develop appropriate charts and apply filters to get a subset of data based on certain conditions.
  • Apply data transformation techniques to convert raw data into meaningful values and build data queries to fetch data from multiple data sources.

    Associated Assessment Criteria for Exit Level Outcomes 3.
  • Utilize statistical knowledge by having familiarity with a variety of statistical techniques.
  • Determine the accuracy of statistical estimations by performing in-depth analyses of the probability of various outcomes.
  • Construct statistical analysis (descriptive and inferential) to find the solutions to various business functions through the use of hypothetical testing.
  • Summarize and forecast analysis using graphical methods to assist in decision-making.
  • Analyse data and draw assumptions and conclusions from the respective data.
  • Build and develop machine learning models using linear algebra-based vectors and matrices.
  • Devise machine learning model algorithm and evaluate the machine learning models by slicing and dicing the data for new opportunities in business prospects.

    Associated Assessment Criteria for Exit Level Outcomes 4.
  • Discuss the principles, characteristics, and features of Python programming as well as the use of application software.
  • Identify program units, data and data structures required to implement a given design.
  • Design a programming solution for a given problem and select and implement control structures to meet the design algorithm.
  • Apply several feature engineering techniques which include selection, manipulation, and transformation of raw data into desired features using a statistical approach.
  • Apply dimensionality reduction technique on the dataset to reduce the number of columns thereby reducing the computational cost of modelling.
  • Perform exploratory data analysis on the dataset to discover trends, and patterns, or to check assumptions with the help of statistical summary and graphical representations.

    Associated Assessment Criteria for Exit Level Outcomes 5.
  • Perform data pre-processing, data analysis and data visualization and discuss the results.
  • Create a report to provide project details, results, discussion and conclusion.
  • Present the topic, EDA steps, results, and conclusion.

    Associated Assessment Criteria for Exit Level Outcomes 6.
  • Discuss what is machine learning, the machine learning process, applications of machine learning and its types.
  • Illustrate understanding of the machine learning models and formulate the problem.
  • Identify the steps to perform EDA and the appropriate ML algorithm to perform model building using SKLearn.
  • Select and apply appropriate model evaluation metrics to evaluate the built model and interpret the result of model evaluation.

    Associated Assessment Criteria for Exit Level Outcomes 7.
  • Illustrate knowledge of and list similarities between artificial neurons and biological neurons.
  • Explain mathematical calculations involved in artificial neuron - summation and activation function.
  • Explain different activation functions, construct or experiment with artificial neural network model by stacking layers of artificial neurons for classification and regression problems as well as functional and sequential APIs of TensorFlow.
  • Experiment with hyperparameters to improve artificial neural network model accuracy.
  • Apply pooling techniques to the output of the intermediate convolution layer to further extract.

    Associated Assessment Criteria for Exit Level Outcomes 8.
  • Perform data pre-processing, data analysis and data visualization and discuss the results.
  • Create machine learning models, test, and evaluate them for data.
  • Create a report to provide project details, results, discussion, and conclusion.
  • Present the topic, implementation of all phases of the data science project life cycle, results, and conclusion. 
    		•

    INTERNATIONAL COMPARABILITY 
    Country: Australia
    Institution name: University of Melbourne
    Qualification type: Graduate Diploma in Data Science
    Duration: One year

    Entry requirements:
  • An undergraduate degree (or equivalent) in any discipline
    AND
  • Successfully completed MAST10006 Calculus 2 AND MAST10007 Linear Algebra (or their equivalents).

    Purpose:
    The Graduate Diploma in Data Science is an ideal starting point for learners who are interested in joining this booming industry and don't have a background in computer science or statistics.
    Through this qualification, learners will develop fundamental skills in both computer science and statistics, so they can keep pace with the rapidly changing demands of a data-driven job market - and world.

    Learners will be shown how to use statistical tools, techniques, and methods along with in-depth analysis and evaluation, learning to solve real-world problems in the data realm.

    Upon completion of the Graduate Diploma, the learner can supercharge their qualification by enrolling in the Master of Data Science (subject to meeting the requirements).

    Course structure:
    Modules:
  • Algorithms and Complexity.
  • Programming and Software Development.
  • Database Systems & Information Modelling.
  • Elements of Data Processing.
  • Algorithms, machine learning and data mining, comparable with Predictive modelling with machine learning.
  • Methods of Mathematical Statistics.
  • A First Course In Statistical Learning.
  • Multivariate Statistics for Data Science.
  • Statistical Modelling for Data Science, comparable with Statistics for Data Science.
  • Computational Statistics & Data Science.

    Similarities:
  • The University of Melbourne (UM) and the South African (SA) qualifications both accept learners who have completed a bachelor's degree in the related field.
  • Both qualifications are completed over one year and have 120 credit offerings.
  • Both qualifications share similar modules such as Statistical Modelling for Data Science, comparable to Statistics for Data Science and Algorithms, and machine learning and data mining, comparable to Predictive modelling with machine learning.
  • The primary purpose of the SA qualification is to provide learners with a specialised grasp of data analytical skills using data science techniques in their respective domains and across a wide range of disciplines and industries.
  • Similarly, for the UM qualification, learners will be shown how to use statistical tools, techniques, and methods along with in-depth analysis and evaluation, learning to solve real-world problems in the data realm.
  • Both qualifications articulate vertically into a master's degree in the related field.

    Differences:
  • The UM qualifications can also be obtained on a part-time basis over two years.
  • The SA qualification has a research component whereas the UM qualification has coursework only.

    Country: New Zealand
    Name of the Institution: University of Canterbury
    Qualification title: Postgraduate Diploma in Applied Data Science
    Duration: One year
    Credits: 120

    Entry requirements:
  • Potential learners can come from a variety of undergraduate backgrounds.
    And
  • Will require a B Grade Point Average in their 300-level bachelor's degree courses or have evidence of achievement at postgraduate level.

    Purpose:
    Data science is a new profession emerging along with the exponential growth in size, and availability of 'big data'. A data scientist provides insight into future trends by looking at past and current data. This is an essential skill set in a world where everything from education to commerce, communication to transport, involves large-scale data collection and digitalisation.

    This Postgraduate Diploma is designed to accommodate learners from a range of backgrounds (not just those with Mathematics, Statistics, and Computer Science majors), who want to enhance or build their data science capabilities and combine these with the skills and knowledge they bring from their previous studies. So long as you are data-hungry and industry-aware; this degree can add to your employability and career prospects.

    Course structure:
    Modules:
  • Introduction to Data Science.
  • Computer Programming.
  • Data Management.
  • Advanced Statistical Modelling.
  • Data Mining.
  • Scalable Data Science.
  • Foundations of Deep Learning, comparable with Deep Learning with artificial neural networks.
  • Python computer programming, comparable with Data Science with Python.

    Similarities:
  • The University of Canterbury (UC) qualification and the South African (SA) qualification are both offered over one year with 120 credits.
  • Both qualifications accept learners who have completed a bachelor's degree.
  • Both qualifications share similar modules such as Foundations of Deep Learning, comparable to Deep Learning with artificial neural networks and Python computer programming, comparable to Data Science with Python.
  • The purpose of the design of the UC qualification is to accommodate learners from a range of backgrounds who want to enhance or build their data science capabilities and combine these with the skills and knowledge they bring from their previous studies.
  • Similarly, the SA qualification aims to enhance the knowledge of prospective learners, give them an awareness of the data-driven world, and make them employable, the qualification is multidisciplinary, it includes, learning how to use data in a business context and how massive data sets can be managed and analysed to make appropriate decisions.
  • Both qualifications articulate vertically into a master's degree.

    Differences:
    The SA qualification has a research component, and the UC qualification has coursework only. 
    		•

    ARTICULATION OPTIONS 
    Horizontal Articulation:
  • Bachelor of Data Science, NQF Level 8.
  • Bachelor of Science Honours in Data Science, NQF Level 8.
  • Postgraduate Diploma in Business Management, NQF Level 8.
  • Bachelor of Business Administration Honours, NQF Level 8.

    Vertical Articulation:
  • Master of Applied Data Science, NQF Level 9.
  • Master of Science in Data Science, NQF Level 9.
  • Master of Science in Computer Science, NQF Level 9.

    Diagonal Articulation
  • Diagonal articulation options are not available. 
    		•

    MODERATION OPTIONS 
    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. Regenesys Management (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.