The programme consists of eight 15 credit modules and a smaller Professional Communication module.
The modules will be presented in blocks of approximately 9 weeks. The typical module consists of a 2-week self-paced pre-reading/pre assignment period which is followed by the lecture block week. Students attend this lecture block week either at Stellenbosch University or online if students cannot travel to Stellenbosch. No further contact time will be required during the post assignment period which lasts approximately 6 weeks.
Modules offered by other departments can have a different structure/presence requirement.
Six compulsory data science modules have to be completed, as well as two generic structured modules.
Because the modules build on each other, they need to be taken in a specific order (see co-requisites).
Furthermore, students will automatically be registered for the Professional Communication 771 module of 1 credit to be completed online.
Note that a certificate of competence for one or the programme related short courses may serve as exemption to the respective module at postgraduate diploma level.
During each module assessments take place during the pre-reading, the lecture block week and the post block assignments to test the application of the theory exposed to in the module. These marks will be combined to give a final module mark.
The following data science modules are all compulsory:
All PGDip students complete six Data Science modules.
For students who started the programme before 2025 these modules are Programming in R, Data Science, Applied Machine Learning, Optimisation, Data Analytics, Big Data Technologies.
For students who will start with the programme from 2025 onwards, the six core modules are Data Science, Applied Machine Learning, Optimisation, Data Analytics, Big Data Technologies, Applied Deep Learning.
| Programming in R (Eng) 774 Dr Vermeulen (Dept of Industrial Engineering) until 2024 |
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| R is a powerful programming language for data analysis and statistical computing. In this module, students will learn to become efficient in programming using R, with a specific focus on developing algorithmic solutions do data analytics problems. The module starts with the basics of R programming for Data Science, covering data types, data objects, control structures and input/output control. A strong focus is placed on using R for data engineering, and to get raw data ready for analytics and predictive modelling. Programs will be developed for visual analytics, data clustering, and for predictive modelling, including linear regression, decision trees, and random forests. |
| Data Science (Eng) 774 Prof Gwetu (Dept of Industrial Engineering) |
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| Data science is the application of computational, statistical, and machine learning techniques to gain insight into real world problems. The main focus of this module is on the data science project life cycle, specifically to gain a clear understanding of the five steps in the data science process, namely obtain, scrub/wrangling, explore, model, and interpret. Each of these steps will be studied with the main purpose to gain an understanding of the requirements, complexities, and tools to apply to each of these life cycle steps. Students will understand the process of constructing a data pipeline, from raw data to knowledge. Case studies from the engineering domain will be used to explore each of these steps. |
| Applied Machine Learning 774 Prof Engelbrecht (Dept of Industrial Engineering) |
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| In this module students will be exposed to a wide range of machine learning techniques and gain practical experience in implementing them. Students will not only learn the theoretical underpinnings of several machine learning techniques, gaining an important understanding of the requirements, inductive bias, advantages and disadvantages, but also will gain the practical know-how needed to apply these techniques to real-world problems. The focus will be on information-based learning, similarity-based learning, error-based learning, kernel-based learning, probabilistic learning, ensemble learning, and incremental learning. |
| Optimisation (Eng) 774 Prof Grobler, Dr Venter (Dept of Industrial Engineering) |
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To master the art of building and solving (exactly or approximately) optimisation models of the following types: Linear programming models, assignment, transportation and transhipment models, and integer programming models. Understanding the different classes of optimisation problems and implementing advanced optimisation algorithms to solve real-world optimisation problems. A student who has successfully completed this module will be able to:
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| Big Data Technologies (Eng) 774 Dr Du Toit (Dept of Industrial Engineering) |
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| This module focuses on the tools and platforms for big data management and processing. Big data management refers to the governance, administration and organization of large volumes of data of different types (both structured and unstructured). Efficient platforms to store and manage big data will be considered, including NoSQL, data warehousing, and distributed systems. Big data processing focuses on the 3V-characteristics of big data namely volume, velocity, and variety. Different architectures for big data processing will be studied, including map-reduce and graphical big data models. Students will obtain experience in big data tools and platforms, including Spark, Hadoop, R, and data virtualization. Other aspects of big data, such as data streams, data fusion, and data sources, including social media and sensor data, will be discussed. |
| Data Analytics (Eng) 774 Prof Engelbrecht & Mr Burger (Dept of Industrial Engineering) |
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| In this module students will learn the data analytics life cycle, and how to apply each phase of this life cycle to solve engineering data analytics problems. Students will learn techniques for exploratory data analysis, and how to apply machine learning approaches for mining knowledge from data sets, to extract hidden patterns, associations and correlations from data. Students will gain the practical know-how needed to apply data analytics techniques to structured data. Students will learn advanced approaches to data analytics, with a specific focus on visual analytics, image analytics, text analytics, and time series analytics. The student will gain experience in the implementation of various techniques to extract meaning from these different data source types. The advanced data analytics techniques encountered will be applied to data intensive engineering problems. |
| Applied Deep Learning (Eng) 774 Mr Burger (Dept of Industrial Engineering) |
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| Deep learning systems are increasingly being applied in industry, ranging from language understanding, speech and image recognition, planning, and autonomous tasks. Through this module, students will develop a theoretical knowledge of deep learning algorithms, gain practical experience in implementing deep learning algorithms and develop practical know-how to apply deep learning to a diverse range of problems and industries. |
Students have to do any two of the following generic faculty master’s modules:
| Project Management 713 Prof T Barnard (Dept of Industrial Engineering) |
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| The module focuses on advanced topics in project management, and it is expected that participants have either attended a project management course or have experience in managing projects. The module builds on the traditional project scheduling by addressing critical chain management and looks at managing project risks through the identification and assessment of risk potentials and mitigating strategies, including resource / cost management and contingency planning. The selection of appropriate teams and structures to facilitate contract management are discussed, along with executing project leadership through proper communication channels. The importance of procurement, from tender procedures through to supplier selection will be highlighted. The different nuances between commercial and research projects will be explained. |
| Industrial Management 744 Prof Pistorius & Prof Grobbelaar (Dept of Industrial Engineering) |
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| The purpose of the module is to present principles of general management within the context of technical disciplines. The course themes include the business environment and strategic management on a firm level, touching on the role of innovation and technology for competitiveness on a systems level from international and national perspectives. The course will include a significant focus on tools and techniques for technology and innovation management exploring the link between technology management and business management taking a capabilities approach. These capabilities include acquisition, protection, exploitation, identification and selection. We relate traditional approaches to technology management to what it means for the context of the fourth industrial revolution, platform economies and innovation platforms. |
| The functions of engineering management, namely planning, organising, leading and controlling will also be discussed. This will include a specific focus on human resource management, both insofar as managing projects, people and groups is concerned as well as aspects of labour relations and specifically the labour law and contractual requirements in South Africa. We contextualise the above under the theme of “leadership”, with an exploration of different leadership styles, communication and motivation. |
The following modules have co-requisites. Especially when considering the part-time option, it is critical for the student to consider the co-requisites of modules at registration, meaning that the student must have previously been registered for the co-requisite module or be registered for it in parallel, irrespective of the performance in the module.
| Modules | Co-requisite Modules |
|---|---|
| Data Science (Eng) 774 | Programming in R (Eng) 774 (until end 2024) From 2025 programming experience is an admission requirement. |
| Applied Machine Learning 774 | Data Science (Eng) 774 |
| Optimisation (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774 |
| Big Data Technologies (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774 (from 2025) |
| Data Analytics (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774, Optimisation (Eng) 774 |
| Applied Deep Learning (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774, Optimisation (Eng) 774 |
To be considered for admission you must:
- Hold at least an approved BTech, BEng, or a BSc degree from a South African university or university of technology; or
- Hold other academic degree qualifications and appropriate experience that have been approved by the Faculty Board. The department’s chairperson must make a recommendation regarding such a qualification and experience to the Faculty Board.
Students must have passed the following 1st year subjects at university level:
- Mathematics, Statistics, Applied Mathematics, or Mathematical Statistics; and
- Computer programming (proof of any assessed programming qualification equivalent to first year tertiary programming will also be considered).
. Students who do NOT meet the computer programming requirements can still qualify for the program by completing one of the following courses:
- MITx: Introduction to Computer Science and Programming Using Python: https://www.edx.org/learn/computer-science/massachusetts-institute-of-technology-introduction-to-computer-science-and-programming-using-python
- CS50’s Introduction to Computer Science https://learning.edx.org/course/course-v1:HarvardX+CS50+X/home
- Expressway to Data Science: Python Programming Specialization https://www.coursera.org/specializations/python-programming-data-science#courses
. The certificate of completion and the mark received should be uploaded along with your academic transcript as one pdf file. After submission of the application, no further documents can be added.
Note that the marks of your last academic degree will be evaluated against an annual minimum requirement, as this is a competitive programme.
See application process here.
Also refer to the postgraduate admission model in Figure 3.1, in Section 3.2 in the Engineering Calendar, reproduced
From PGDip to MEng(research)
If you want to further your academic journey at the IE department you can, with a strong PGDip degree, apply for the MEng (Industrial Engineering) research.
For consideration, the final marks average of the 6 core modules of your PGDip degree must be at least 65%.
You need to have a potential Data Science supervisor.
Find information on the MEng (Industrial Engineering) research here.
Contact: Melinda Rust @ mrust@sun.ac.za
Note: It is not possible to pursue the MEng (IE) structured programme at the IE department when you graduate with the PGDip (IE) as the programmes are thematically too close.
