African Data Science Academy
The African Data Science Academy's mission is to facilitate human capacity building in data science and computational thinking at the University, nationally in South Africa, across Africa and globally. It develops and presents open courses offered by the School as well as bespoke courses developed for our industry and academic partners. We invite you to explore partnering with the African Data Science Academy for your training needs.
Previously offered Courses
Presenter(s)
| Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
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Duration
| 5 days from 16th to 20th of August |
Cost
| R6,000.00
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Format
| The course takes place online from 09 - 13 August 2021. The course consists of online videos and synchronous lectures/workshops.
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Requirements
| Participants must have one year of university mathematics. Programming experience not necessary.
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Target audience
| This course is for any postraduate students and professionals in healthcare and the life sciences who wishes to learn the fundamentals of statistical tests commonly used in research. The course develops an intuitive understanding of biostatistics, without the burden of mathematical rigor.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant across health and life sciences.
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General Description
- This is a course that teaches the fundamentals of statistical analyses commonly used in healthcare and the life sciences. As a professional in these fields, we rely heavily on the published literature to inform our practice and to stay abreast of new findings. As such, it is of vital importance to be able to interpret the research questions, the study design, the methods employed to conduct the study, and the results. This requires a thorough understanding of the statistics.
Outcomes
This course covers statistics. The outcomes include"
By successfully completing this course you will have a deep appreciation for, and understanding of, statistical analysis. This includes an understanding of common statistics such as p values, t test, confidence intervals, logistic regression, and many more.
At the end of this course, you will know about study design, randomization, data collection, summary statistics, and the creation of graphs and plots. You will know how to conduct the most commonly used statistical tests in the literature and understand how to interpret the results.
Your Presenter, Dr Juan KlopperDr Juan Klopper is a Surgeon with a deep interest in data science. He holds a MBChB from the University of Pretoria and a MMed(Surg) cum laude from the University of Free State. He has developed multiple popular, online courses in data science.
Presenter(s)
| Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
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Duration
| 5 days from August 30 – September 3 |
Cost
| R6,000.00
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Format
| The course takes place online from 02 - 05 August 2021. The course consists of online videos and synchronous lectures/workshops.
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Requirements
| Participants must have one year of university mathematics. Programming experience not necessary.
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Target audience
| This course is aimed at undergraduate students, postgraduate students, staff, and practitioners.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant across all disciplines.
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General Description
- This course starts by developing an intuitive understanding of the basics of the mathematics involved. It requires only high school mathematics, and the intuition is only built to provide for a better understanding of neural networks. The substance of the course is then about learning how to write short lines of Python code to construct neural networks. The course concentrates on two use cases.
- Firstly, we look at building predictive models using structured data. This is the sort of data captured in a spreadsheet. We will use existing data from which our neural network will learn to predict an outcome variable. This is the same technology that predicts what you may want to watch next on your favourite streaming service or recommends what you might want to purchase on your favourite online shopping site. It can also learn to predict patient outcomes, having learnt from existing patient data.
- The second case is that of computer vision. We will train a model to recognises malignant skin lesions from photos. This is a very common task in machine learning and deep neural networks are particularly adept at computer vision. They power self-driving cars after all.
Outcomes
This course covers Deep Learning. The outcomes include:
- develop a deep appreciation of the inner workings of neural networks as they pertain to structured data and to images;
- become familiar with the different types of learning in artificial intelligence, how to work with data, how to create neural networks,
- become familiar with how to train neural networks on existing data, and how to test their accuracy.
Your Presenter, Dr Juan KlopperDr Juan Klopper is a Surgeon with a deep interest in data science. He holds a MBChB from the University of Pretoria and a MMed(Surg) cum laude from the University of Free State. He has developed multiple popular, online courses in data science.
Presenter(s)
| Mr Hamman Schoonwinkel, School of Accountancy, Stellenbosch University
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Duration
| 01 September - 31 December 2021 |
Cost
| R2,000.00
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Format
| The course takes place online from 01 September - 31 December 2021. The course consists of online videos and synchronous online debates and Q&A sessions.
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Requirements
| No prerequisite. |
Target audience
| Beginners with no prior knowledge.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant for all who require an introductory overview of blockchain technologies.
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General Description
This short course is designed as an introductory overview to Blockchain Technologies in the context of finance. A participant to this course will gain knowledge in the following aspects:
- Technical workings of a blockchain (theoretical level);
- The purpose and affordances of blockchain;
- How Bitcoin fits into the system of money;
- Legal implications and considerations of crypto assets;
- Frequent topics of debate, e.g. electricity usage.
Outcomes
Your Presenter, Mr Hamman Schoonwinkel
This course will be presented by Hamman Schoonwinkel,who is a CA(SA) and lecturer at Stellenbosch University. He recently completed the Global Master's in Blockchain Technologies, presented by Zigurat Innovation & Technology Business School.
Presenter(s)
| Department of Philosophy, Stellenbosch University- Dr Tanya de Villiers-Botha
- Prof. Louise du Toit
- Dr Susan Hall
- Prof. Johan Hattingh
- Dr Andrea Palk
- Prof. Vasti Roodt
- Prof. JP Smit
- Prof. Anton van Niekerk
- Prof. Minka Woermann
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Duration
| 18 - 22 October 2021
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Cost
| R4,000.00
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Format
| The course consists of online videos and synchronous lectures/workshops. |
Requirements
| None
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Target audience
| Industry, students and other stakeholders in data science who need to broaden their knowledge of data ethics.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant across all data science areas.
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General Description
- It has become clear that the practicing of data science cannot be divorced from the ethical considerations. This short course provides an introduction to the ethics of data science. Participants will be given an overview of foundational ethical theory, which will then be applied to practical ethical concerns that arise in the context of data science, with the help of topical case studies. There will be specific focus on the South African and African contexts.
Outcomes
Your Presenter, Dr Tanya de Villiers-BothaDr Tanya de Villiers-Botha will be presenting this course, together with her colleagues from the Department of Philosophy at Stellenbosch University. More information on the Department's world class academics can be found on their
Department of Philosophy.
Presenter(s)
| Prof Sheung Yin Kevin Mo |
Duration
| October 25 - 29 |
Cost
| R8,000.00
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Format
| The course consists of online videos and synchronous lectures/workshops. |
Requirements
| A bachelors degree with a year of mathematics, applied mathematics or statistics.
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Target audience
| Industry, students and other stakeholders interested in data science applied to finance/investments.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant for those interested in finance/investment and in data science.
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General Description
- This course investigates methods implemented in multiple quantitative trading strategies with emphasis on automated trading and quantitative finance based approaches to enhance the trade-decision making mechanism. The course provides a comprehensive view of the algorithmic trading paradigm and some of the key quantitative finance foundations of these trading strategies. Topics explore markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods.
Outcomes
At the end of th ecourse, the participants would have explored markets, financial modeling and its pitfalls, factor model based strategies, portfolio optimization strategies, and order execution strategies. The data mining and machine learning based trading strategies are also introduced, and these strategies include, but not limited to, weak classifier method, boosting, neural network and genetic programming algorithmic emerging methods.
Your Presenter, Prof Sheung Yin Kevin Mo
Prof Sheung Yin Kevin Mo graduated with an undergraduate degree in Systems Engineering and in Economics from the University of Virginia in the USA. He holds a masters degree from the same institution. He graduated with a PhD(financial engineering) from the Stevens Institute of Technology in the USA. He past industry experience include Investment Vice President at Quantitative Management Associates in New Jersey, USA. He is currently an Adjunct Industry Professor at Stevens Institute of Technology.
Presenter(s)
| Mr Hans-Peter Baker and Prof Sugnet Lubbe |
Duration
| October 11 - December 10 |
Cost
| R6,000.00
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Format
| The course consists of online and synchronous lectures/tutorials. |
Requirements
| General computer literacy as well as some prior knowledge of first year mathematics and statistics taught at a university.
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Target audience
| Industry, students and other stakeholders interested in data science applied to finance/investments.
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Certificate
| Attendance Certificate.
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Focus disciplines
| This course is relevant for those interested in strengthening their research capacity in terms of basic statistical understanding and its application using R.
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General Description
- This course, presented over nine weeks, offers an introduction into the application of the programming language R to statistical analysis. R is statistical software particularly powerful in data analysis and graphical representation.
Outcomes
Your Presenter - Mr Hans-Peter Bakker and Prof Sugnet Lubbe
Mr Hans-Peter Bakker will be the principal presenter of this course. He is currently a full-time lecturer in the Department of Statistics and Actuarial Science at Stellenbosch University. He will be supported by Professor Sugnet Lubbe, also at Stellenbosch University's Department of Statistics and Actuarial Science.
Presenter(s)
| Prof Jacomine Grobler, Dr Sydney Kasongo, Dr Thorsten Schmidt-Durant |
Duration
| November 15 - 19 |
Cost
| R6,500.00 for attendance option, R8,000 for competence certificate
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Format
| Pre-recorded lectures and daily live sessions
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Requirements
| Bachelor's Degree
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Target audience
| Graduates working in Industry, staff members and those considering postgraduate studies in Data Science
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Certificate
| Attendance or Competence Certificate.
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Focus disciplines
| Industry (graduates) who have encountered or been exposed to data science, without having proper knowledge of the field or the process of facilitating a data science projects.
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General Description
The short course is designed as an introductory overview to data science explained at the hand of the data science project life cycle. A participant to this
course will gain knowledge in the following aspects:
- The data science project life cycle and the different role players involved,
- The aspects included in each of the data science project life cycle phases,
- The technologies applicable to the data science project life cycle,
- The di erent data formats and the requirements imposed by these formats on data science technologies,
- The process of constructing a data pipeline from raw data to knowledge, and
- The ethical challenges faced in data science, as well as data regulation and information privacy.
Outcomes
Your Presenter - Prof Jacomine Grobler, Dr Sydney Kasonga and Dr Thorsten Schmidt-Durant
Prof Jacomine Grobler is an associate professor in the Department of Industrial Engineering at Stellenbosch University. Dr Sydney Kasonga is a lecturer and Dr Thorsten Schmidt-Durant is a postdoctoral fellow in the same department. All three teach on the postgraduate data science programmes on offer by the Department.
Presenter(s)
| Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
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Duration
| 5 days from 26 - 30 July 2021
Mon 8:30 to 9:30 introductory session
Mon to Fri, 2pm to 4pm workshop session
The course is packed, so one must have enough time to watch the videos and attempt the practice questions everyday before the 2pm session. |
Cost
| Free.
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Format
| The course takes place online from 26 - 30 July 2021 The course consists of online videos and synchronous lectures/workshops.
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Requirements
| Participants must have one semester of university mathematics. Programming experience not necessary.
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Target audience
| This course is aimed at all students, staff, and practitioners.
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Certificate
| Attendance certificate from the National Institute of Theoretical and Computational Sciences for those who participate fully.
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Focus disciplines
| This course is relevant across all disciplines.
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General Description
This course covers an introduction to data science and computational thinking. No knowledge of programming is required. We will cover
the basics of data science and programming using Python;
- introduce data and data structures;
- introduce Python programming;
- explore relationship between data using Python.
Outcomes
This course covers the basics of data science through Python programming. The outcomes include:
- basic Python programming;
- understand the benefit of data science;
- basic data science models.
Introduction to Cloud Computing with Azure
Presenter(s)
| Mr Luca Steyn, Department of Statistics and Actuarial Science, Stellenbosch University
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Duration
| 4.5 days from 5 - 9 October 2020 |
Cost
| R1050 including the Microsoft certification vouchers needed to write the formal accreditation examination.
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Format
| The course takes place online from 5 – 9 October 2020 1 orientation session + 3 one-hour of Q&A sessions over 5 days - Compulsory On-boarding takes place the week of 1 October
- Delegates are required to test their MS Teams compatibility, attend the orientation session (a maximum of 45 minutes) and fully complete the on-boarding sessions before the course commences to ensure that we are able to resolve any accessibility issues in good time.
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Requirements
| Participants must have a background in computer science, information systems, engineering, and/or data science.
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Target audience
| This course is aimed at honours, masters and Phd students, final year students in 4 year degrees, staff, and practitioners.
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Certificate
| Certificate of attendance from SU and the opportunity to write the formal Microsoft Azure examination for accreditation through Microsoft.
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Focus disciplines
| This course is relevant across all disciplines.
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General Description
This course covers the fundamentals of Microsoft Azure. No knowledge of the cloud or software engineering is required. We will cover
the basics of cloud concepts;
- explore Microsoft Azure cloud services;
- explore how security is a shared responsibility;
- learn to apply and monitor infrastructure standards;
- learn to control and organise cloud resources;
- investigate how Microsoft Azure helps us predict the cost of a solution.
Outcomes
This course covers the fundamentals of Microsoft Azure. No knowledge of the cloud or software engineering is required. We will cover
the basics of cloud concepts:
- Learn cloud concepts such as High Availability, Scalability, Elasticity, Agility, Fault Tolerance, and Disaster Recovery
- Understand the benefits of cloud computing in Azure and how it can save you time and money
- Compare and contrast basic strategies for transitioning to the Azure cloud
- Explore the breadth of services available in Azure includ-ing compute, network, storage, and security
Course Includes
- A 'voucher' to write the formal Microsoft Azure examination for accreditation through Microsoft.
