Postgraduate Diploma in Engineering

Programme Content

The programme consists of eight 15 credit modules, that will be presented in blocks (each block has a duration of one week), and students must attend these blocks at Stellenbosch University. No further contact time will be required.

Six compulsory data science modules have to be completed, as well as two generic structured modules.

For each module, a number of assignments will be completed to test the application of the theory exposed to in the module.

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 in the Introduction to Data Science short course as presented by the School for Data Science and Computational Thinking may serve as exemption to the Data Science (Eng) 774 module.

Compulsory Data Science Modules

The following data science modules are all compulsory:

Programming in R (Eng) 774    *Dr Schmidt-Dumont (Industrial Engineering)
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 Grobler & Dr De Kock (Industrial Engineering)
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 & Dr Schmidt-Dumont (Industrial Engineering)
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 Van Vuuren, Mr Nel & Dr De Kock (Industrial Engineering)
In this module students will learn about different classes of optimisation problems that can occur in the engineering domain, and will learn how to characterise the complexities of these optimisation problems. The student will learn a wide range of advanced meta-heuristics and hyper-heuristics that can be used to solve these different classes of optimisation problems. The student will gain experience in implementing advanced optimisation algorithms to solve real-world engineering optimisation problems. As one of the application areas, the module will explore ways in which optimisation techniques can be applied to improve the performance of machine learning algorithms, and to easily adapt machine learning approaches to non-stationary environments and data streams.
Big Data Technologies (Eng) 774    Dr Du Toit (Industrial Engineering)
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 (Industrial Engineering)
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.

Compulsory Generic Structured Master’s Modules

Students have to do any two of the following generic faculty master's modules:

Project Management 713    Department of Industrial Engineering
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    Department of Industrial Engineering
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.

Module Co-Requisites

A student may choose to enrol for the programme as a full-time or a part-time student, where the main difference is the length of time allowed to enrol. It is expected of a full-time student to complete the programme in one years, however, the student may apply for a second year with good motivation. A part-time student is expected to complete the programme in two years, however, the student may, with good motivation, apply for extension.

In considering the part-time option, it is critical for the student to consider the co-requisites of modules, 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. The module co-requisites are as follows:

Modules Co-requisite Modules
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, Optimisation (Eng) 774
Data Analytics (Eng) 774 Data Science (Eng) 774, Applied Machine Learning 774, Optimisation (Eng) 774

Admission Requirements

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 1st year

  • Mathematics, Statistics or Applied Mathematics

Computer Programming experience will be an advantage.

Also refer to the postgraduate admission model in Figure 3.1, in Section 3.2 in the Engineering Calendar, reproduced below: Fig 3.1