[Registered Qual & Unit Std Home page] [Search Qualifications] [Search Unit Standards]
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
122721  Postgraduate Diploma in Data Science 
ORIGINATOR
Stadio (Pty) Ltd 
PRIMARY OR DELEGATED QUALITY ASSURANCE FUNCTIONARY NQF SUB-FRAMEWORK
-   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 0526/24  2024-08-22  2027-08-22 
LAST DATE FOR ENROLMENT LAST DATE FOR ACHIEVEMENT
2028-08-22   2031-08-22  

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 Science is to enable working professionals to undertake advanced study in the field of data science. Data-driven decisions are at the centre of a range of business functions and as such, the field of data science is at the intersection of business domains, coding, mathematics, and statistics. The qualification has a strong interdisciplinary character and is relevant to graduates in applied mathematics, finance, computer science, economics, and other related disciplines.

The qualification will equip learners with the necessary skills to extract, clean, prepare and pre-process data for feeding into the best-suited algorithm(s), and finally for presenting the data in appropriate visual and written formats. The qualification will provide learners with theoretical knowledge and practical experience of the widely used supervised and unsupervised learning algorithms. Learners will be taught to present the final information in the format of a fit-for-purpose report, by depicting results in appropriate tables, supported by an interpretation of results. These skills, combined with domain knowledge, will provide the prospective data scientist with the necessary skills to provide practical solutions and future predictions to complex numerical problems in business, and any field where data is used extensively.

On completion of the qualification, successful learners will be able to:
  • Apply different data exploration methods to gain a critical understanding of data, variables, and observations.
  • Draft a data science project plan to apply decision-making skills to solve complex problems.
  • Demonstrate understanding of data science methods and techniques.
  • Apply data management skills to code, prepare, pre-process, manipulate, and visualise data in preparation for the model-building phase.
  • Build, fit, and tune models with respect to different data science methods.
  • Interpret and critically evaluate solutions to which a particular data science method has been applied in practice.
  • Apply the built models on unused data and use domain knowledge to provide decision-makers with accurate predictive information to enable the formulation.

    Rationale:
    South Africa is inundated with data, constantly moving, and renewing itself in a data-driven world. Society needs personnel who can apply critical, analytical tools and algorithms to model and explain patterns observed in data using mathematical and statistical techniques. This qualification will enable the learner to work with data using Microsoft Excel and Python, manage and use data, and apply a variety of statistical learning algorithms such as Logistic Regression, Support Vector Machines, Decision Trees, Boosting Algorithms, Neural Networks, K-Means Clustering and Hierarchical Clustering. Learners will furthermore gain knowledge in the domain area of either finance or marketing via the elective module, to enable them to function optimally as data scientists in these fields.

    The qualification will enable the individual working in most sectors or departments such as Financial, Banking, Insurance, Marketing, Tourism, Meteorological, Health, Information Technology (IT) and Business, to drive data-driven decisions for those holding managerial positions. There are many Data Science jobs advertised on a multitude of platforms. The enormous amount of data available needs to be mined, cleaned, fitted, trained, and evaluated using appropriate algorithms so that key decisions and future predictions can be made. From banking to transport and shopping, everyday activities are increasingly leaving digital footprints that are transforming society's households and workplaces. The richness of data is changing the dynamics of many professions, and employers are increasingly seeking workers who can help them make sense of it.

    The qualification is ideal for learners with a strong proficiency in analytical skills who have an affinity and feel for making sense of large amounts of data. The candidates will gain experience in reading, cleaning, exploring, fitting, training, and evaluating large structured and unstructured data sets to reduce the data into a usable form so that key decisions can be extracted from the raw information. Successful candidates will be able to operate in any field or sector where large amounts of data are available, which includes most areas of industry such as banking, insurance, finance marketing, tourism, meteorological, health, IT, business, municipalities, and housing.

    The qualification will enable learners to pursue the following careers:
  • Data Scientist.
  • Data analyst.
  • Data engineer.
  • Statistical Learning Specialist.
  • Machine Learning Specialist.
  • Senior Data Scientist.
  • Specialist Data Scientist.
  • Principal Data Scientist.
  • Data and Analytics Manage.
  • Python Data Scientist.

    Data Science is one of the most popular emerging domains and most sought-after career options. Data Science is revolutionising almost every industry and dominates emerging job rankings. The fourth industrial revolution (4IR) with an enormous amount of data available and exponential increases in computing power appropriately qualified individuals need to mine this data to identify hidden patterns and extract actionable insights. Managers and decision-makers in most sectors rely on qualified personnel with well-developed analytical skills to analyse, present and communicate results to optimise and improve productivity.

    The most appropriate learning pathway would reside in any one of finance, marketing, business, or information technology. A background in statistical, mathematical or computer science would be optimal but not a necessity. There are many data scientists who learn the skill of data science through postgraduate degrees, diplomas, or courses. A person with a strong domain knowledge of the sector in which he/she works such as the banking industry forms a prominent pillar in the art of being a data scientist, computer science, statistics, and mathematics.

    Society will benefit from these highly sought-after professionals who can effectively operate in the 4IR with the availability of mega data and a continued exponential increase in computing power. The professionals will contribute to society in many ways, from simple and yet important tasks such as traffic control, customer service, online buying habits, advertising, hospital waiting time, predicting the weather and exchange rate to more advanced tasks such as self-driving cars, fraud detection, energy exploration, robotics, and genetic manipulation. Subsequently, the economy will grow in many ways due to improved efficiency, effectiveness, decisions, productivity, and profitability in all sectors. 
    		•

    LEARNING ASSUMED TO BE IN PLACE AND RECOGNITION OF PRIOR LEARNING 
    Recognition of Prior Learning (RPL):
    The institution has an approved Recognition of Prior Learning (RPL) policy applicable to equivalent qualifications for admission into the qualification. RPL will be applied to accommodate applicants who qualify. RPL thus provides alternative access and admission to qualifications, as well as advancement within qualifications.

    RPL for access:
  • Applicants who do not meet the stated admission criteria, but who have relevant work experience/prior learning may apply for admission under the policy on Recognition of Prior Learning (RPL).
  • The institution admits a maximum of 10% per cohort via RPL.
  • The implementation of RPL is context-specific, in terms of discipline, programme and level.

    RPL for exemption of modules:
  • In specified circumstances qualifying applicants may also engage in the RPL for exemption process, where any form of informal, formal, or non-formal learning will be assessed for relevance towards possible module exemption.

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

    RECOGNISE PREVIOUS LEARNING? 
    Y 

    QUALIFICATION RULES 
    This qualification consists of the following compulsory and elective modules at NQF Level 8 totalling 120 Credits.

    Compulsory Modules, Level 8, 100 Credits:
  • Working with Data, 20 Credits.
  • Introduction to Data Science and Statistics, 20 Credits.
  • Data Science I, 20 Credits.
  • Data Science II, 20 Credits.
  • Capstone Project, 20 Credits.

    Elective Modules, Level 8, 20 Credits (Select one module from the following options):
  • Data Science in Finance, 20 credits.
    OR
  • Data Science in Marketing, 20 credits. 
    		•

    EXIT LEVEL OUTCOMES 
    1. Apply different data exploration methods to gain a critical understanding of data, variables, and observations.
    2. Demonstrate the ability to draft a data science project plan to apply decision-making skills to solve complex problems.
    3. Demonstrate an understanding of data science methods and techniques.
    4. Apply data management skills to code, prepare, pre-process, manipulate, and visualise data in preparation for the model-building phase.
    5. Demonstrate the ability to build, fit, and tune models with respect to different data science methods.
    6. Demonstrate the ability to interpret and critically evaluate solutions to which a particular data science method has been applied in practice.
    7. Apply the built models on unused data and use domain knowledge to provide decision-makers with accurate predictive information to enable the formulation of important decisions. 

    ASSOCIATED ASSESSMENT CRITERIA 
    Associated Assessment Criteria for Exit Level Outcome 1:
  • Analyse the nature and structure of data.
  • Apply descriptive statistical techniques to explore the data.
  • Apply data exploring and visualising techniques using Python and associated libraries.

    Associated Assessment Criteria for Exit Level Outcome 2:
  • Discuss the data science process.
  • Draft a data science project plan.

    Associated Assessment Criteria for Exit Level Outcome 3:
  • Discuss the concepts related to data science and statistics.
  • Analyse a range of algorithms available to solve data-related problems.

    Associated Assessment Criteria for Exit Level Outcome 4:
  • Apply appropriate data manipulation techniques to prepare data for analysis.
  • Use appropriate tools to apply statistical and mathematical operations to prepare data.
  • Produce graphical representations to assist with the preparation, pre-processing, and cleaning of data.

    Associated Assessment Criteria for Exit Level Outcome 5:
  • Explain backpropagation, gradient descent, learning rate and cost functions.
  • Build, fit, and train models related to logistic regression, support vector machines, decision trees, boosting algorithms, neural networks, K-Means clustering and hierarchical clustering.
  • Assess model performance and variance/bias trade-off.
  • Configure and tune the parameters.
  • Apply strategies to manage the complexities of these algorithms.

    Associated Assessment Criteria for Exit Level Outcome 6:
  • Evaluate and interpret models related to logistic regression, support vector machines, decision trees, boosting algorithms, neural networks, K-Means clustering, and hierarchical clustering.
  • Evaluate the performance of certain model solutions compared to others for a particular data science problem.
  • Synthesise information into a well-written report using written and graphical techniques.

    Associated Assessment Criteria for Exit Level Outcome 7:
  • Apply the models to make predictions using unused data.
  • Together with domain-specific knowledge, provide bulleted practical solutions and recommendations to the client.
  • Present the algorithm ready to use for future predictions.
  • Present all information, analytical procedures, and findings to a panel of academics and industry-specific experts. 
    		•

    INTERNATIONAL COMPARABILITY 
    The qualification is comparable to similar qualifications offered in the following countries.

    Country: United Kingdom
    Institution: University of London
    Qualification Title: Post Graduate Diploma in Data Science
    Duration: One year full time
    Entry Requirements:
  • Bachelor's degree from an acceptable university in any of the following subjects: Business and Management, Education, English, Geography, History, Humanities, Law, Philosophy, Psychology, Theology, Social Sciences or Sociology.

    Purpose.
    The qualification develops analytical and critical skills, providing learners with the tools and competencies needed to intelligently interrogate numerical, textual, and qualitative data. This includes extracting meaning from raw information and communicating the results of their investigations and their implications to stakeholders or other interested parties. These skills can lead to a variety of careers with small and large technology firms, the biomedical research sector, the charitable and voluntary sector, and the public research sector.

    Qualification structure:
    The qualification consists of the following compulsory and elective modules.

    Compulsory Modules:
  • Statistics and Statistical Data Mining compared to Introduction to Data Science and Statistics.
  • Machine Learning.
  • Data Programming in Python I compared to Introduction to Python Programming.
  • Big Data Analysis.
  • Data Visualisation.
  • Data Science Research Topics compared to Capstone Project.

    Elective Modules (Select two modules):
  • Natural Language Processing
  • Social Networks and Graph Analysis
  • Artificial Intelligence
  • R for Data Science compared to Data Science
  • Neural Networks
  • Blockchain Programming
  • Financial Data Modelling compared to Data Science in Finance
  • Financial Markets compared to Data Science in Finance
  • Mathematics for Data Science

    Assessment:
    Each module is assessed by an unseen written exam, which is usually held in May or June.

    Similarities:
  • The University of London (UL) and the South African (SA) qualifications are offered over one year.
  • Both qualifications require candidates who completed a Bachelor's degree in data science or equivalent qualification in a related field.
  • Both qualifications share a similar purpose.
  • Both qualifications consist of compulsory and elective modules.

    Country: United States of America
    Institution: Purdue University
    Qualification Title: Post Graduate Diploma in Data Science
    Duration: 44 weeks
    Entry Requirements:
  • A bachelor's degree with an average of 50% or higher marks a basic understanding of programming concepts and mathematics
  • Working Professionals with 2+ years of experience are preferred to apply for this program. The Data Science role requires an amalgam of experience.

    Purpose:
    Data Science knowledge and using the correct tools and technologies. It is a solid career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind A Data Scientist collects, analyses, and interprets complex data to inform business decisions. They employ statistical techniques, machine learning, and data visualization to uncover insights and trends. Strong programming, data manipulation and domain knowledge are crucial for success in this role This qualification will open pathways for a career in virtually every realm of business from healthcare to education to manufacturing.

    Qualification structure:
    The qualification consists of the following compulsory modules.

    Compulsory Modules:
  • Programming Refresher.
  • Statistics essential for Data Science compared Introduction to Data Science and Statistics.
  • Python for Data Science compared to Data Science.
  • Applied Data Science with Python compared to Data Science.
  • Machine Learning.
  • Data Science Capstone Project compared to Capstone Project.

    Similarities:
  • The Purdue University (PU) and the South African (SA) qualifications require candidates who completed a Bachelor's degree in data science or equivalent qualification in a related field.
  • Both qualifications share a similar purpose.
  • Both qualifications articulate vertically to a Bachelor's degree or equivalent qualification in a related field.

    Difference:
    The PU qualification is offered over 44 weeks of full-time study whereas the SA qualification is offered over one year.

    Country: Australia
    Institution: University of South Australia
    Qualification Title: Graduate Diploma in Data Science
    Duration: 1 year full-time
    Entry requirements
    Applicants to the qualification are required to have:
  • A Bachelor's degree or equivalent from a recognised higher education institution with a minimum of one year of full-time study in Mathematics Information Technology Data Science or a combination thereof.
    Or
  • A Graduate Certificate in Data Science or equivalent from a recognised higher education institution.

    Purpose/Rationale:
    Data Scientists are in strong demand to manage, analyse and use the data collected to create predictive models. However, there is currently a significant shortage of data scientists globally and companies are actively looking for talented professionals to analyse big data and inform their business decisions. There is currently a high demand for data scientists in Australia.

    Vast volumes of data are generated every day around the globe. The need to make sense of it has given rise to the revolutionary area of 'Big Data', and to a new career of 'data scientist'. Data scientists find patterns, making meaning and drawing value from the seeming chaos. In the qualification, learners will be taught current techniques in data science and how to apply this knowledge professionally.

    Learners will develop:
  • Cognitive skills to review, analyse, consolidate, and synthesise knowledge and identify and provide solutions to complex problems in data science.
  • Cognitive skills to think critically and to generate and evaluate complex ideas.
  • Specialised technical and creative skills in data science.
  • Communication skills to demonstrate an understanding of theoretical concepts.
  • Communication skills to transfer complex knowledge and ideas to a variety of audiences.

    This graduate diploma is offered as part of a suite of three qualifications (graduate certificate, graduate diploma, and master's). Each qualification extends to the next, learners can easily transition to a master-level qualification.

    Qualification structure:
    The qualification consists of the following compulsory modules.

    Compulsory Modules:
  • Big Data Concepts compared to Working with Data.
  • Statistical Programming for Data Science.
  • Statistics for Data Science compared to Introduction to Data Science and Statistics.
  • Predictive analytics.
  • Unsupervised analytics in Data Science compared to Data Science.
  • Research Methods compared to Capstone Project.
  • Data Visualisation.

    Elective Modules:
    Learners must choose a module from the Directed Elective groups, in consultation with and approval of the Program Director. The chosen electives should complement current knowledge and experience.

    Similarities:
  • The University of South Australia (UniSA) and the South African (SA) qualifications are offered over 1-year full-time study.
  • Both qualifications require candidates who completed a Bachelor's degree in Data Science and the related field.
  • The UniSA and SA qualifications develop similar learning outcomes and have the same purpose and rationale of addressing a significant shortage of data scientists.
  • Both qualifications articulate vertically to a Master's degree in Data Science and related qualifications.
  • The UniSA and SA qualifications consist of compulsory and elective modules.

    Difference:
  • The UniSA qualification is offered as part of a suite of three qualifications (graduate certificate, graduate diploma, and master's) whereas the SA qualification is a standalone qualification and not offered as part of a suite of three qualifications.

    Conclusion:
    It is evident from the analysis that the SA qualification is comparable to the above qualification in the duration, purpose, rationale, qualification structure, and articulation. 
    		•

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

    Horizontal Articulation:
  • Bachelor of Science Honours in Data Science, NQF Level 8.
  • Postgraduate Diploma in Computer Science, NQF Level 8.
  • Bachelor of Science Honours in Information Technology, NQF Level 8.
  • Bachelor of Science Honours in Computer Science, NQF Level 8.
  • Bachelor of Science Honours in Computer Science and Information Systems, 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.
  • Master of Information Technology, NQF Level 9.
  • Master of Science in Information Technology Management, NQF Level 9.

    Diagonal Articulation
    There is no diagonal articulation for this qualification. 
    		•

    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.
     
    NONE 


    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.