Yearbooks

Programme: MSc eScience (Coursework)

Code Faculty Duration Credits Download
02250193 Faculty of Natural and Agricultural Sciences Minimum duration of study: 2 years Total credits: 180

Programme information

The curriculum for the MSc (eScience) coursework degree programme comprises 180 credits of coursework and a research component. One of the key features of the curriculum is a capstone project that runs parallel with coursework modules in the first year of study. During the capstone project, students will go through the entire cycles of solving a real-world data science problem, collecting and processing real-world data, designing methods to solve the problem, and implementing a solution. The capstone project and coursework prepare the student for the mini-dissertation problem supervised by an expert. 

Admission requirements

The admission requirements are:

  •      an honours degree in either statistics, mathematics, computer science, physics, or related fields; AND demonstrable knowledge of basic principles of probability and statistics, computing, calculus and linear algebra;

           OR

  • passing an entrance evaluation designed by the academic advisory committee of the programme within the consortium. An average of 65% at honours level is the minimum for consideration,  although admission will be competitive and an honours average of at least 70% is highly recommended.
  • Student numbers are limited to a maximum of 30.
  • Admission is additionally dependent on availability of supervisor/s and/or projects within the participating departments.
  • Historical performance during prior studies will also be considered in selecting students. Specific attention will be given to modules repeated and duration of study.
  • The research proposal of applicants should be in line with the research focus of the participating departments.
  • Any further additional entrance requirements as specified by the head of department in consultation with the departmental postgraduate selection committee.
  • The head of department, in consultation with the departmental postgraduate selection committee and participating departments reserves the right to prescribe additional modules.

Other programme-specific information

Candidates are required to familiarise themselves with the General Regulations regarding the maximum period of registration and other requirements for master’s degrees.

Promotion to next study year

The progress of all master’s candidates is monitored biannually by the supervisor and the postgraduate coordinator. A candidate’s study may be terminated if the progress is unsatisfactory or if the candidate is unable to finish his/her studies during the prescribed period.

Subject to exceptions approved by the dean, on recommendation of the head of department, and where applicable, a student may not enter for the master’s examination in the same module more than twice.

Pass with distinction

The MSc degree is conferred with distinction to candidates who obtain a final average mark of at least 75%, with a minimum of 65% in each module, and a mark of at least 75% for the mini-dissertation from each of the members of the examination panel. Where a member of the examination panel awards a mark of less than 75% for the mini-dissertation, that member of the examination panel must offer, in writing, support for his/her decision, or indicate in writing that he/she supports the examination committee’s decision to confer the degree with distinction.

Minimum credits: 90

Choose 4 modules to the value of 60 credits from the list of electives.

Core modules

  • Module content:

    Scientific writing styles; layouts for assignments, projects, theses or publications; research methodologies; scientific assignments; integration of all the aforementioned content items for a capstone project in data science.

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  • Module content:

    Technical processes of data collection, storage, exchange and access; Ethical aspects of data management; Legal and regulatory frameworks in South Africa and in relevant jurisdictions; Data policies; Data privacy; Data ownership; Legal liabilities of analytical decisions and discrimination; and the Technical and algorithmic approaches to enhance data privacy, and relevant case studies.

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Elective modules

  • Module content:

    Introduction: Basic concepts. Supervised learning setup: Least means squares, logistic regression, perceptron, exponential family, generative learning algorithms, Gaussian discriminant analysis, naïve Bayes, support vector machines, model selection and feature selection. Learning theory: bias/variance tradeoff, union and Chernoff/Hoeffding bounds, VC dimension, worst case (online) learning. Unsupervised learning: clustering, k-means, expectation maximisation, mixture of Gaussians, factor analysis, principal components analysis, independent components analysis. Reinforcement learning and control: Markov decision processes, Bellman equations, value iteration and policy iteration, Q-learning, value function approximation, policy search, reinforce, partially observable Markov decision problems.

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  • Module content:

    Data and image models; visualisation attributes (colour) and design (layout); exploratory data analysis; interactive data visualisation; multidimensional data; graphical perception; visualisation software (Python & R); and types of visualisation (animation, networks and text).

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  • Module content:

    Introduction to scientific computing architectures in Python, introduction to distributed systems, introduction to distributed databases, introduction to parallelism, large-data computation and storage models, introduction to well-known distributed systems architectures, and programming large-data applications on open-source infrastructures for data processing and storage systems.

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  • Module content:

    High-dimensional space, best-fit subspaces and singular value decomposition, random walks and Markov chains, statistical machine learning, clustering, random graphs, topic models, matrix factorisation, hidden Markov models, graphical models, wavelets, and sparse representations.

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  • Module content:

    Specialised and applied concepts and trends in data science.

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  • Module content:

    An understanding of multivariate statistics, hypothesis testing and confidence intervals. The ability to model data using well-known statistical distributions as well as the ability to handle data that is both continuous and categorical. The ability to perform statistical modelling including multivariate linear regression and adjust for multiple hypotheses. Forecasting, extrapolation, prediction and modelling using statistical methods. Bayesian statistics, an understanding of bootstrapping and Monte Carlo simulation.

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  • Module content:

    Introduction to convex optimisation, subgradient methods, decomposition and distributed optimisation, proximal and operator splitting methods, conjugate gradients, and nonconvex problems.

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Minimum credits: 90

Fundamental modules


The information published here is subject to change and may be amended after the publication of this information. The General Regulations (G Regulations) apply to all faculties of the University of Pretoria. It is expected of each student to familiarise himself or herself well with these regulations as well as with the information contained in the General Rules section. Ignorance concerning these regulations and rules will not be accepted as an excuse for any transgression.

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