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​​​2021​​​​

African ​Da​​​ta Science Acade​my​​
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​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.​

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​​ ​​Courses​
The following courses will be offered in 2021.​​​​​​​
​​ ​​​Full Course Information​​​

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​ Pres​enter(s)
​Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
​ Duration
​5 days from 16th​ to 20th of August​
​​Cost
R6,000.00
​ Format
The course takes place online from 09 - 13 August 2021.
The course consists of online videos and synchronous lectures/workshops.
​ Requirements
Participants must have one year of university mathematics. Programming experience not necessary. 
​ 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.
​ Certificate
Attendance Certificate.
​ Focus disciplines
This course is relevant across health and life sciences.
​Course outline
​Click here​.

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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 Klopper
Dr 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. ​​





​ Pres​enter(s)
​Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
​ Duration
​5 days from August 30 – September 3
​​Cost
R6,000.00
​ Format
The course takes place online from 02 - 05 August 2021.
The course consists of online videos and synchronous lectures/workshops.
​ Requirements
Participants must have one year of university mathematics. Programming experience not necessary. 
​ Target audience
​This course is aimed at undergraduate students, postgraduate students, staff, and practitioners.
​ Certificate
Attendance Certificate.
​ Focus disciplines
This course is relevant across all disciplines.
​Course outline
Click here.

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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 Klopper
Dr 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. 


​ Pres​enter(s)
​Mr Hamman Schoonwinkel, School of Accountancy, Stellenbosch University
​ Duration
01 September - 31 December 2021
​​Cost
R2,000.00
​ 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.
​ Requirements
No prerequisite.
​ Target audience
Beginners with no prior knowledge.
​ Certificate
Attendance Certificate.
​ Focus disciplines
This course is relevant for all who require an introductory overview of blockchain technologies.
​Course outline
​Click here. For an introductory video, click here​.

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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
  • The course provides an introductory overview of Blockchain Technologies.

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.





​ Pres​enter(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
​ Duration
18 - 22 October 2021
​​Cost
R4,000.00
​ Format
The course consists of online videos and synchronous lectures/workshops.
​ Requirements
None
​ Target audience
Industry, students and other stakeholders in data science who need to broaden their knowledge of data ethics.
​ Certificate
Attendance Certificate.
​ Focus disciplines
This course is relevant across all data science areas.
​Course outline
​Click here​.

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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
  • Participants will gain competency in the following:

    • Familiarity with introductory ethical theory
    • Familiarity with introductory data ethics
    • Familiarity with prominent ethical concerns relating to data practices, for example, ownership of data, the governance of data practices, data and privacy, algorithmic bias, artificial moral decision making, best practice and the like.
    • The ability to apply ethical theory to practical ethical problems that stem from data-related practices​

Your Presenter, Dr Tanya de Villiers-Botha
Dr 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.




​ Pres​enter(s)
​Prof Sheung Yin Kevin Mo
​ Duration
October 25 - 29
​​Cost
R8,000.00
​ Format
The course consists of online videos and synchronous lectures/workshops.
​ Requirements
A bachelors degree with a year of mathematics, applied mathematics or statistics.
​ Target audience
Industry, students and other stakeholders interested in data science applied to finance/investments.
​ Certificate
Attendance Certificate.
​ Focus disciplines
This course is relevant for those interested in finance/investment and in data science.
​Course outline
​Click here​​.

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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.




​ Pres​enter(s)
​Mr Hans-Peter Baker and Prof Sugnet Lubbe
​ Duration
October 11 - December 10​
​​Cost
R6,000.00
​ 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.
​ Target audience
Industry, students and other stakeholders interested in data science applied to finance/investments.
​ Certificate
Attendance Certificate.
​ 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.
​Course outline
​Click here​​.

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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
  • Participants who complete this course should be able to perform basic data manipulation; descriptive data analyses including graphical representations; and some basic inferential statistics in R. It should also offer a sufficient platform on which to further develop their competencies in R to handle more advanced applications on their own.

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.






​​
​ Pres​enter(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
​ Format
Pre-recorded lectures and daily live sessions
​ Requirements
​Bachelor's Degree
​ Target audience
Graduates working in Industry, staff members and those considering postgraduate studies in Data Science​
​ Certificate
Attendance or Competence Certificate.
​ 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 project​
​Course outline
​Click here​​.

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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
  • Participants who complete this course should attain knowledge of data science project life cycle, its technologies and processes.

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.


|Registration to open soon​|




​ Pres​enter(s)
​Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch University
​ Duration
October 4 to November 1st
Cost
R1,500​​
​ Format
​Live lecture sessions every Monday from 6pm to 8pm. Q&A sessions every Thursday from 6pm to 8pm.
The course consists of online videos, exercises and synchronous lectures/workshops. Please note, this course requires a lot of guided learning using lecture notes and videos prior to each two-hour lecture.​
​ Requirements
Participants must have one semester of university mathematics. Programming experience not necessary. 
​ Target audience
​This course is aimed at all students, staff, and practitioners. Maximum number of student - 300.
​ Certificate
Attendance certificate
​ Focus disciplines
This course is relevant across all disciplines.​
Course outline
Click here​

​​​​​​​​​

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.

Your Presenter, Dr Juan Klopper
Dr 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.