The importance of pursuing a postgraduate qualification has become more evident with most of the careers showcased in Career Paths, requiring a minimum of a postgraduate qualification (for example: Honours Degree, Postgraduate Diploma, Master’s Degree or a PHD).
You can find out more about the various postgraduate courses at University of Stellenbosch:
Degree Description
The purpose of this programme is to train Quantitative Risk Analysts and is of an advanced mathematical and Statistical nature.
The program consists of 8 modules (90 credits): Two modules in Financial Risk Management (Derivative pricing, VaR, Volatility, Credit risk) and modules in Portfolio Management, Retail Credit Risk modelling, Practical Financial modelling using Excel, VBA and R, Financial Mathematical Statistics (Financial Mathematics and Extreme Value Theory), Stochastic Simulation, and Time Series Analysis and an assignment (research project) (30 credits).
Admission Requirements
BCom (Mathematical Sciences) degree with minimum pass requirement of an average of 60% in Financial Risk Management 3 modules and Mathematical Statistics 3 modules as well as pass mark in Financial Mathematics 3
Funding information
Bursary office of the University
Programme Administrator
Prof WJ Conradie
wjc@sun.ac.za
Degree Description
Data is important and is analysed in almost all environments. A data scientist must have the following skills: to gather data and to store it, to transform data and graphically represent it, to ask relevant questions and to analyse data so as to answer decision-making questions. Data scientists are employed as statisticians, data analysts, data managers and statistical analysts in, for example, the marketing, information and management positions of firms. In this capacity they form part of the exciting management and decision-making processes in large organisations. Students with this training can negotiate exciting and well-paid career opportunities for themselves.
Coursework modules consist of Biostatistics, Multivariate Analysis, Time series analysis, Data Mining, Stochastic simulation, Survival analysis and Introduction to Statistical Learning.
Admission Requirements
Bachelor degree with minimum pass requirement of 65% in Mathematical Statistics 3 or Statistics 3.
Funding information
Bursary office of the University
Programme Administrator
Prof S Lubbe
slubbe@sun.ac.za
Degree Description
Data Engineering contains all the tasks required to make data available for analysis, knowledge discovery and decision-making processes. The most important task of the data engineer is to develop and maintain an organization’s data pipeline systems, and implement algorithms to transform data into a usable format for analysis. The tasks of a data engineer include data collection, data storage, data synchronization, data transformation, data cleaning, data management, and data model development.
Data engineers are responsible for detecting trends in data sets and developing algorithms to make raw data usable. This requires a considerable set of technical skills, including in-depth knowledge of database design and various programming languages. Data engineers are often responsible for building algorithms to provide easier access to structured and unstructured data, but it requires an understanding of the goals of an organization using large datasets. Data engineers need excellent communication skills to connect with different stakeholders inside and outside the organization to understand what Big Data business leaders want to earn and also to present their findings in a way the audience can easily understand.
From the first year, the focus area in Data Engineering is built on the foundations of Mathematics, Statistics, Computer Science and Artificial Intelligence. Students develop the engineering skills to create mathematical, physical, and statistical models of real systems, including data systems. After obtaining the qualification, Stellenbosch University’s Data Engineers can integrate these areas of knowledge to critically analyse complex systems to come up with innovative solutions to problems. This will be demonstrated by students who have successfully completed their final-year data engineering project in their final year of study.
Admission Requirements
- National Senior Certificate with admission to bachelor’s studies, or an exemption certificate issued by the Matriculation Board
- An average, using the six best matric subjects (excluding Life Orientation and Mathematical Literacy), of at least 70%
- Mathematics with at least 70% (or in the Senior Certificate Examination before or in 2007, Mathematics HG: at least a B
- Physical Sciences with at least 60% (or in the Senior Certificate Examination before or in 2007, Physical Science HG with at least a C)
- English Home Language: 50%, with no Afrikaans requirement;Â or
English First Additional Language: 60%, with no Afrikaans requirement;Â or
English First Additional Language: 50%, together with Afrikaans Home Language: 50%;Â or
English First Additional Language: 50%, together with Afrikaans 2nd Additional Lang.: 60%
Prospective students who meet the above admission requirements must also be selected before they can be admitted
Funding information
Bursary office of the University
Programme Administrator
Prof HA Engelbrecht
hebrecht@sun.ac.za
Degree Description
Data science (DS) is the scientific investigation that employs innovative approaches and algorithms, most notably machine learning algorithms, for processing and analysing data. DS technologies can be applied to both small and big data, of various types such as relational, images, video, audio, and text. Big data constitutes extremely large data sets that may be analysed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions.
This programme focuses on enabling students to develop innovative optimisation and machine learning techniques to produce novel, efficient and robust data science technologies, for use in Industrial Engineering, Engineering Management and related applications.
Admission Requirements
To be considered for admission you must:
- Hold at least a BEng, a BScHons, another relevant four-year bachelor’s degree, an MTech, or a PGDip (Eng); 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 is also an advantage.
Funding information
http://www.ie.sun.ac.za/
Programme Administrator
Postgraduate Manager:
Melinda Rust:Â mrust@sun.ac.za
Degree Description
The Masters programme is structured with a component of coursework (8 modules – 120 credits) and a research component i.e.  a 60 credit research assignment. The coursework component consists of 2 modules in Extreme Value Theory and modules in xVa, Datamining, VaR, Financial Modelling with Phyton, Alternative investments and advanced Credit Risk Modelling.
Admission Requirements
BComHons (Financial Risk Management) or Honours degree in Financial Engineering
Funding information
Bursary office of the University
Programme Administrator
Prof WJ Conradie
wjc@sun.ac.za
Degree Description
The Masters programme is structured with a component of coursework and a research component. Students register for either a 90 credit (research orientated) )thesis or a 60 credit research assignment. The coursework component consists of modules in Bootstrap and resampling methods, Extreme value theory, Multidimensional scaling, Statistical Learning theory and Applied Statistical Learning theory.
The focus of the Masters programme is on multidimensional visualizations and Statistical Learning, the core elements of Data Science.
Admission Requirements
Honours degree with majors in Statistics or Mathematical Statistics.
Funding information
Bursary office of the University
Programme Administrator
Prof S Lubbe
slubbe@sun.ac.za
Degree Description
Data science (DS) is the scientific investigation that employs innovative approaches and algorithms, most notably machine learning algorithms, for processing and analysing data. DS technologies can be applied to both small and big data, of various types such as relational, images, video, audio, and text. Big data constitutes extremely large data sets that may be analysed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions.
This programme focuses on enabling students to develop innovative optimisation and machine learning techniques to produce novel, efficient and robust data science technologies, for use in Industrial Engineering, Engineering Management and related applications.
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 is also an advantage.
Funding information
http://www.ie.sun.ac.za/
Programme Administrator
Data Sciences Program Head:
Prof Andries Engelbrecht:Â engel@sun.ac.za
Postgraduate Manager:
Melinda Rust:Â mrust@sun.ac.za