PGDip (Ing) (Bedryfsingenieurswese)
The programme consists of eight 15 credit modules and a smaller Professional Communication module.
The modules will be presented in blocks of ca. 9 weeks. The typical module consists of a 2-week self-paced pre-reading/pre assignment period which is followed by either a lecture block week and a post assignment period, which lasts ca. 6 weeks, or the pre-reading period is followed by smaller lecture blocks (2 days, 2 days, 1 day) spread across 4 weeks (see timetable). Each smaller block is followed by a ca. 2 week post assignment.
Students must attend all five lecture days full time. The lecture days take place online. In addition to the online presentation, limited modules may also be delivered in person. Beside the lecture days/week no further contact time will be required.
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.
The module content builds on previous modules. Therefore, most modules must 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.
To be considered for admission for the year 2027 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, 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
In this case the programming certificate and the marks received must be uploaded along with your academic history on the application portal before you submit your application. Current SU student who experience issues with upload may contact [email protected] After submission of the application, no further documents can be added to your application.
Note that the marks of your last academic degree will be evaluated against an annual minimum requirement, as this is a competitive programme. For the intake 2027 the average of your last academic year must be at least 65%.
See application process here.
Also refer to the postgraduate admission model in Figure 3.1, in Section 3.2 in the Engineering Calendar, reproduced
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. Also see assessment information for core data science modules.
Die volgende ses datawetenskapmodules is almal verpligtend:
Vir studente wat die program voor 2025 begin het, is hierdie modules Programmering in R, Datawetenskap, Toegepaste Masjienleer, Optimalisering, Data-analise, Grootdatategnologieë.
Vir studente wat die program vanaf 2025 begin het, is die ses kernmodules Datawetenskap, Toegepaste Masjienleer, Optimalisering, Data-analise, Grootdatategnologieë, Toegepaste Diep Leer.
Die volgende twee generiese modules is verpligtend:
The following modules have co-requisites. It is critical for the student to consider the co-requisites of modules at registration, meaning that the student must have previously passed the co-requisite module or be registered for it in parallel in the given year, irrespective of the performance in the module.
The 60 credit research assignment is an exception here the pre-modules must be passed.
| Modules | Co-requisite Modules (status Jan 2026) |
|---|---|
| Data Science (Eng) 774 | Programming experience at 1st year university level 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 |
| Data Analytics (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774 |
| Applied Deep Learning (Eng) 774 | Data Science (Eng) 774, Applied Machine Learning 774 |
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 @ [email protected]
Note: It is not possible to pursue the MEng (IE) structured Data Science programme at the IE department when you graduate with the PGDip (IE) as the programmes are thematically too closely aligned.