Masters in Data Science - M.Eng (Structured): Industrial Engineering Focus: Data Science - Stellenbosch University
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Masters in
Data Science
M.Eng (Structured): Industrial Engineering
Focus: Data Science
Host: Department of Industrial Engineering
(in collaboration with other Engineering Departments,
Applied Mathematics and Computer Science)
Stellenbosch University
1
in early detection of changes in the production system
Introduction performance and minimization of expensive wasted
production time.
Data science (DS) is the scientific investigation that 4. Supply chain performance prediction
employs innovative approaches and algorithms, most
notably machine learning algorithms, for processing and The process of delivering a product from customer order
analysing data. DS technologies can be applied to both to order delivery involves a large number of resources
small and big data, of various types such as relational, and incorporates a large amount of uncertainty. Different
images, video, audio, and text. Big data constitutes supply chain partners e.g. warehouses, transport services,
extremely large data sets that may be analysed manufacturers
computationally to reveal patterns, trends and and more may
associations, especially relating to human behaviour and be involved in a
interactions. specific order.
DS techniques
This programme focuses on enabling students to develop can add immense
innovative optimisation and machine learning techniques value in making
to produce novel, efficient and robust data science sense of the
technologies, for use in Industrial Engineering, Engineering millions of
Management and related applications. records of
Examples include: supply chain data
1. Forecasting generated on a
Forecasting customer daily basis.
demand requires the 5. Process Monitoring
analysis of a large Advances in online monitoring and data collection present
amount of data and is an opportunity to enhance the efficiency, sustainability
essential to operating and profitability of chemical and mineral processing plants.
an efficient business For example, condition-based maintenance is an approach
which meets to monitor the condition of assets and inform decisions
customer regarding planned preventative maintenance; machine
requirements. learning techniques can be used to provide deeper insights
Retailers conduct into equipment conditions using all available
millions of measurements. Another example includes operation state
transactions on a identification, whereby normal operating conditions
daily basis generating (NOC) are defined based on feature extraction from
datasets which suppliers then can utilize to understand measured data. Any deviation from NOC can be used to
demand patterns and schedule production and deliveries. identify potential faults, and the origin of the fault can be
2. Customer segmentation and targeted traced using causality analysis. These are but a few of the
marketing machine learning techniques currently applied in the
A key concept in the business world is that not all process engineering industry.
customers are the same. Different customers may have a
different impact on a company’s profitability and each Structured Program Contents
customer will most often demonstrate unique buying
behaviour. DS techniques can play a significant role in The program consists of 8 x 15 credit modules, that will
determining the optimal segmentation of customers, be presented in blocks (each block has a duration of 1
where companies often have hundreds of customers week), and students must attend these blocks at
which need to be serviced. Furthermore, customer order Stellenbosch University. A project in each module will
history can be used to predict future behaviour and then be required to test the application of the theory
develop targeted marketing strategies. exposed to in the module.
3. Production quality control A final project must then be completed, where the
knowledge gained in all 8 modules can be applied on a
A large number of quality control and assurance systems
relevant industry related project.
have been developed over the last couple of decades to
monitor production and predict when a production The detailed course content is as follows:
system will no longer meet specifications. The use of
more intelligent DS algorithms can play a significant role
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Module Description Credits Responsible
Generic Structured Masters Modules:
Students need to select 2 out of the 4 modules below
Project The module focuses on advanced topics in project management, and it is expected 15 Industrial
Management that participants have either attended a project management course or have Engineering
51993-873 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.
Advanced Topics in The purpose of the module is to present principles of general management within 15 Industrial
Engineering the context of technical disciplines. The course themes include the business Engineering
Management environment and strategic management on a firm level, touching on the role of
11478-873 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.
Numerical Methods The module focuses on matrix computations. We study the effective solution of 15 Applied
TW876 linear systems, involving both square and rectangular matrices (least-squares). Mathematics
Direct as well as iterative methods are considered, with the emphasis on sparse
matrices and matrices with structure. Numerical methods for the eigenvalue
problem are also considered. Pitfalls such as numerical instability and ill-conditioning
are pointed out. Model problems are taken from partial differential equations, data
analysis and image processing. Theory, algorithmic aspects, and applications are
emphasized in equal parts.
Project Finance The module focuses on how to finance a business opportunity (project) that can be 15 Civil
58157-812 isolated from the rest of a company’s business activities. Financing through a Engineering
combination of debt and equity are discussed, based on the future profitability of
the project where project cash flow is the main source of capital recovery and the
project assets are the only collateral. The concepts of construction loans and public-
private partnerships are discussed. A number of case studies will be covered in the
module.
Module content:
• Infrastructure and development finance: Sources of business finance and
private sector project financing models.
• Review of time value of money / discounted cash flow / interest calculations.
• Basic accounting statements (balance sheet, income and cash flow statements.
• Costing and management accounting – theory / techniques and costing system
concepts.
• Ratio analysis, from basic ratios to the DuPont approach.
• Economic analysis of investment decisions.
• Market valuation (EVA and MVA).
• Value drivers in the company, sustainability and the Balanced Scorecard.
• The national accounts and economic growth.
• Feasibility studies and techno economic analysis:
• System identification, parameter identification, environment and system
boundary
• Definition, environmental scanning, system modelling and simulation concepts,
modelling
• Risk and uncertainty in infrastructure finance and project development.
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Module Description Credits Responsible
• Materials, labour and equipment: Impact of required service and quality levels.
Cost estimation and cost controls of construction projects.
• Revenue stream estimating and modelling. Financing models.
Compulsory Modules
Data Science Data science is the application of computational, statistical, and machine learning 15 Jacomine
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 In this module students will be exposed to a wide range of machine learning 15 Andries
Learning techniques and gain practical experience in implementing them. Students will not Thorsten
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.
Big Data This module focuses on the tools and platforms for big data management and 15 External
Technologies processing. Big data management refers to the governance, administration and Specialist
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 for In this module students will learn the data analytics life cycle, and how to apply each 15 Andries
Engineers 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.
Optimisation for In this module students will learn about different classes of optimisation problems 15 Jan
Data Science 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.
Selected Specialization Module – Students need to select one of the choices below:
The availability and time schedules of these courses must be confirmed before enrolment. (Some of the modules may be
semester modules)
Advanced Design This course aims to expose students to the solution of optimization problems in 15 Mechanical and
814 engineering. The course will follow a systematic approach to solve optimization Mechatronic
problems: Engineering
• identify optimization problems in engineering,
• construct mathematical programming problems,
• select an appropriate optimization strategy,
• obtain a solution to the mathematical programming problem, and
• interpret the solution of the mathematical programming problem.
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Module Description Credits Responsible
Robotics 814 Mathematical modelling of robots; Rigid motions and homogeneous 15 Mechanical and
transformations; Forward and inverse kinematics; Denevit-Hartenberg convention; Mechatronic
Velocity kinematics: the Jacobian, singularities; Path and trajectory planning; Engineering
Independent joint control; Robot dynamics: Euler-Lagrange equations, kinetic and
potential energy, equations of motion, properties of robot dynamic equations,
Newton-Euler formulation; Force control; Computer Vision: camera calibration,
image segmentation, vision and servo control.
Advanced Control Contents: This module is concerned with control systems design and analysis for 15 Mechanical and
Systems 814 MIMO (multi-input-multi-output) systems with uncertainties. It covers basic linear Mechatronic
algebra, block diagram algebra for MIMO systems, loop shaping analysis and design, Engineering
internal stability, generalized Nyquist stability criterion, all stabilizing controllers,
MIMO robustness, generalized plant, linear fractional transformation (LFT),
nominal/robust stability/performance (NS, NP, RS, RP), representing uncertainties,
optimal control (LQ, Kalman filter and LQG), and H-infinity optimal control.
Advanced Dynamics Formulate and solve the dynamics of a particle or system of particles: Relative to 15 Mechanical and
814 static or moving axis system; in terms of generalized coordinates and constraints; Mechatronic
in terms of virtual displacement and work; in terms of the Lagrange and Hamilton Engineering
energy principles; for impulsive forces. Formulate and solve the kinematics and
dynamics of a rigid body: In terms of rotation kinematics; with the modified Euler
rotation equations of motion; for impulsive forces and moments.
Foundations of Many view the advent of deep learning as a revolution that has fundamentally 15 Applied Maths
Deep Learning transformed modern ML and AI. This module will cover the basics of deep learning
as a precursor for more advanced modules in the MSc programme. It will start with
a quick recap of ML fundamentals, namely training, generalisation, overfitting, cross-
validation, regularisation, and hyper-parameter optimisation. The following topics
specific to neural networks will then be covered: multi-layer perceptrons, deep
feedforward neural networks, gradient-based training and backpropagation,
convolutional neural networks, recurrent neural networks, attention mechanisms,
autoencoders and deep generative models.
Probabilistic Probabilistic modelling and reasoning form a cornerstone of modern ML and AI. 15 Applied Maths
Modelling and The module will recap relevant concepts from Probability Theory, including Bayes’
Reasoning theorem and conditional independence. It will then cover marginalisation, sum-
product decomposition, Markov blankets, classic hidden Markov models,
expectation-maximisation, probabilistic graphical models and data completion. The
module will also cover basic information theory, including entropy, mutual
information and Shannon’s theorem.
Applied Machine ML and AI drive the back-ends and front-ends of many large online companies and 15 Applied Maths
Learning at Scale are set to play a transformative role in the “internet of things”. This is a practical
module that looks at how ML is applied to internet-scale systems. Topics covered
will include A/B testing, ranking, recommender systems, and the modelling of users
and entities that they engage with online (like news stories). Network effects, social
networks, online advertising, and ML for real-time auctions will also be covered.
Computer Vision Computer Vision has long been an important driving force for advances in Machine 15 Applied Maths
Learning and have been instrumental in the rise and development of deep learning.
The module will start with convolutional neural networks for image classification,
and extensions like dropout, batch normalisation, data augmentation, transfer
learning, and visual attention. Other typical Computer Vision tasks will then be
overviewed, including object segmentation, colourisation, style transfer, and
automated image captioning. Finally generative models for Computer Vision,
including variational autoencoders and generative adversarial networks, will be
covered.
Natural Language Parsing, understanding, and generating natural human language is a crucial 15 Applied Maths
Processing component of AI. Advances in deep learning are beginning to enable impressive
end-to-end language understanding systems. Topics in this module will include
word embeddings and representations (e.g. word2vec), part-of-speech tagging and
syntactic parsing, topic modelling, language modelling with recurrent and
convolutional neural networks, machine translation with seq2seq models and
attention, and sentence classification in applications like sentiment labelling and
language identification.
Sequence Modelling This module deals with techniques to model and predict temporally varying data, 15 Applied Maths
such as financial time series data, weather data, audio and video signals. It begins
with classical state-space models, linear as well as hidden Markov models, then
recurrent neural networks and the most common modular ways of constructing
them, e.g. with long short-term memory gates. Concurrent neural network
architectures, like the Transformer model, that processes the symbols of an entire
sequence concurrently (or in a series of concurrent passes) will also be covered,
and the module ends with methods to combine the above.
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Module Description Credits Responsible
Monte Carlo Monte Carlo methods form a family of stochastic approximation techniques, and 15 Applied Maths
Methods are widely used in fields spanning Physics, Statistics and Finance. It is also a principal
tool for statistical inference in ML. The module will cover Markov chains and Monte
Carlo methods. These include Metropolis-Hastings methods, Gibbs sampling and
collapsed Gibbs sampling, with applications to topic modelling and non-parametric
Bayesian inference. Topics like importance sampling, slice sampling and exact
sampling will also be included. Inference techniques for normalising constants
(model scores), annealing and thermodynamic integration will also be covered.
Project
Data Science Students will be required to apply and consolidate the knowledge gained throughout 60 Host Industrial
Project this program. For this purpose, students will solve a real-world engineering data Engineering,
science project, providing solutions for each step of the data science project life with study
cycle. As outcome of this project, students will produce a dissertation, describing leaders in all
all of the life cycle phases and research conducted in order to provide a solution to engineering
the specific data science problem. departments
It is encouraged that the knowledge gained in specialisation module selected above, and Computer
is utilised in the project. Science
he rapidly progressed through the ranks, his last two
Academic Profiles of Core Module positions being Head of Department (2009 to 2017) and
Presenters Director: Institute for Big Data and Data Science (2017 to
2018). After 21 years at Tuks, the opportunity arose to
Prof Andries Engelbrecht return to his alma mater. His appointment at Stellenbosch
is an A-rated researcher as University comprises two aspects; 50% is allocated to his
rated by die National Research role as Chair in Data Science in the Faculty of Engineering
Foundation (NRF). This rating and 50% as an academic in the Department of Computer
acknowledges that he is a Science in the Faculty of Science.
leading international researcher "Regarding my position as Chair, my main aim is to promote
in his field. His fields of Data Science within Stellenbosch University. This includes the
expertise are Computational transfer of knowledge to undergraduate and postgraduate
intelligence, Swarm intelligence, students as well as to industry. In order to do this, I want to
Evolutionary computation, Neural networks, establish a research group within the Department of Industrial
Optimisation, Machine learning and Data analytics. Engineering. I have two bright young colleagues in my team,
Prof Engelbrecht is the first incumbent of the new Voigt Prof Jacomine Grobler and Dr Thorsten Schmidt-Dumont. We
Chair in Data Science in the Department of Industrial already have quite a number of master's students enrolled for
Engineering. 2019.”
As a first-year Matie student in 1988 he opted for BSc He presents Data analytics to third-year industrial
with Computer Science and Mathematics. He obtained his engineering students and plans to lead this program as the
honours cum laude in 1992, followed by a master's cum academic program head.
laude (1994) and a PhD in 1999. The research interests of Prof Jan H
During his postgraduate studies he taught Computer van Vuuren are combinatorial
Studies and Mathematics on a temporary basis at a few optimisation and decision support
schools. His introduction to academia came with his within the wider area of operations
appointment as a lecturer in Computer Science at Unisa research. He heads the Stellenbosch
in 1996 where he stayed for two years. Unit for Operations research in
Engineering (SUnORE) within the
His academic career gained momentum in 1998 when he
Department of Industrial Engineering.
joined the University of Pretoria as a lecturer in
Computer Science. Over the two decades that followed
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He obtained his bachelor's degree in science cum laude Dr Thorsten Schmidt-Dumont
from Stellenbosch University in 1989, majoring in is the first recipient of a postdoctoral
mathematics and applied mathematics. He followed this fellowship with a focus on machine
up with an honours degree cum laude in 1990 and a learning applications within the
master's degree cum laude in 1992, both in applied Department of Industrial Engineering.
mathematics and both from Stellenbosch University. He His areas of expertise include the
obtained his doctorate in mathematics from the application of reinforcement learning
University of Oxford, United Kingdom in 1995. He has algorithms to complex problems.
been a member of staff at Stellenbosch University since Additionally, he has a background in
1996, first in the Department of Applied Mathematics approximate optimisation techniques, particularly
(until 2007) and then at the Department of Logistics (until metaheuristics, and computer simulation.
2013). He is currently professor of operations research Dr Schmidt-Dumont was born and raised in Namibia,
within the Department of industrial Engineering, a before moving to Stellenbosch to start his tertiary
position he has held since 2014. In all of these education, opting for a degree in Industrial Engineering
departments he has taught subjects related to based on the wide applicability of the taught skillset in
optimisation, at both undergraduate and postgraduate various industry sectors. He obtained his degrees from
levels. Stellenbosch University (SU): BEng cum laude 2015, PhD
His academic passions are research and postgraduate 2018.
students. He is the author of close to a hundred journal Highlights throughout his under- and postgraduate studies
publications and has supervised (or co-supervised) 29 include being awarded the prize for the best final-year
doctoral students to the successful completion of their project in Industrial Engineering in 2015, a project for
studies. which he was also awarded the prestigious Gerhard
Prof Jacomine Grobler joined Geldenhuys Medal by the Operations Research Society of
the Department of Industrial South Africa for the best 4th year project countrywide in
Engineering at Stellenbosch on 1 Operations Research completed in 2015. Buoyed by this
February 2019 as Associate success at undergraduate level he started his postgraduate
Professor. Her areas of expertise journey, completing a master’s degree, which was
are the development of designated as the runner-up for the prestigious Theodor
optimisation algorithms and the Stewart medal, awarded by the Operations Research
application of data science within Society of South Africa, for the best master’s project
the field of industrial engineering. With this background countrywide in 2018, which he was able to upgrade to a
she joined the newly-established Data Science research PhD in 2018.
group. During the final year of completing his PhD he was
Prof Grobler was born and raised in Pretoria. She decided appointed as a part-time junior lecturer in Industrial
to study engineering as the work is creative and requires Engineering at Stellenbosch University, teaching industrial
good problem-solving skills. Furthermore, she opted for programming at second year level.
Industrial Engineering in particular, because it is people
oriented and there are vast opportunities in industry for Admission Requirements and Fees
industrial engineers.
She obtained all her industrial engineering degrees at the To be considered for admission you must:
University of Pretoria (UP): BEng 2006, HonsBEng 2007, • Hold at least a BEng, a BScHons, another relevant
MEng 2009 and PhD 2015. four-year bachelor’s degree, an MTech, or a PGDip
During her study career, she received 12 awards. These (Eng); or
include the Department of Industrial and System • Hold other academic degree qualifications and
Engineering prize for the best final-year project; Medal appropriate experience that have been approved by
from the South African Institute of Industrial Engineering the Faculty Board. The department’s chairperson
for the best final-year Industrial Engineering student; must make a recommendation regarding such a
Winner of the SAS Operations Research National Student qualification and experience to the Faculty Board.
Competition best honours project; South African Students must have passed 1st year Mathematics, Statistics
Association for the Advancement of Science Bronze or Applied Mathematics. Computer programming
medal for the best dissertation at master’s level at the experience is also an advantage.
University of Pretoria; and the 2017 South African
Also refer to the post graduate admission model in Figure
Institute for Industrial Engineering Award for Outstanding
Young Industrial Engineering Researcher. 3.1, in Section 3.2 in the Engineering Calendar.
Fees are adjusted annually, and the expected fees for 2020
From August 2008 to March 2014 she was employed by
will thus be adjusted in line with university policy. The
Denel Dynamics Pty Ltd. Thereafter she joined the CSIR
as research group leader: Transport and Freight Logistics. current fees for an MEng (Structured) in 2019 are shown
In October 2015 she returned to academia, this time as in the table below.
lecturer in supply chain management at UP. This means that if you study full-time and manage to
complete the program in 1 year, your total fee will be
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R27 008 + R55 800 (for 180 credits at R310 per credit) • Year 2: R27 008 + R18 600 = R45 608
for a total fee of R82 808. • Year 3: R27 008 + R18 600 = R45 608
A more reasonable approach may be to spread this over • Total fee: R136 824
2 years, but then there will be an additional registration The main difference between part-time and full-time is the
fee of R27 008. The fee will then be: length of time you are allowed to enroll. We expect a full-
• Year 1: R27 008 + R27 900 = R54 908 time student to finish the program within 2 years, but
• Year 2: R27 008 + R27 900 = R54 908 he/she may apply for a 3rd year, with a good motivation. A
• Total fee: R109 816 part-time student is allowed to be registered for a longer
period, as shown in the above table.
Similarly, if the program is spread over 3 years part-time:
• Year 1: R27 008 + R18 600 = R45 608
Registration Year 1 2 3 4 5 6
Full-time enrolment R27 008 R27 008 R29 709 NA NA NA
Plus Cost per credit R310 R310 R310 NA NA NA
Part-time enrolment R27 008 R27 008 R27 008 R29 709 R32 679 R35 947
Plus cost per credit R310 R310 R310 R310 R310 R310
you with filling in the application forms and will submit
your application for the acceptance process.
Program Status
This program is in a “pending final approval” status (May
Contacts
2019). Final approval will only happen in November 2019,
• Data Sciences Program Head:
but we believe this is a low risk.
Prof Andries Engelbrecht: engel@sun.ac.za
The first intake for January 2020 will be limited to 30 • Postgraduate Manager:
students. Melinda Rust: mrust@sun.ac.za
• Other contacts:
Enrolment • Prof Jan van Vuuren: vuuren@sun.ac.za
• Prof Jacomine Grobler: jacominegrobler@sun.ac.za
For general enquiries about the program, and to find out
• Dr Thorsten Schmidt-Dumont: thorstens@sun.ac.za
more, contact Prof Andries Engelbrecht.
• Departmental Chair:
To assist you with the enrolment process, contact Prof Corne Schutte: corne@sun.ac.za
Melinda Rust, our Postgraduate Manager. She will assist • Tel nr: (021) 808 4234
• Web: www.ie.sun.ac.za
Version 4 June 2019
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