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African ?Da???ta Science Acade?my??
? Pres?enter(s) | ?Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch 中国体育彩票 |
? 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. |
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- 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.
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.
? Pres?enter(s) | ?Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch 中国体育彩票 |
? 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. |
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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.
- 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.?
? Pres?enter(s) | ?Mr Hamman Schoonwinkel, School of Accountancy, Stellenbosch 中国体育彩票 |
? 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. |
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General Description
- 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?.
The course provides an introductory overview of Blockchain Technologies.
? Pres?enter(s) | ?Department of Philosophy, Stellenbosch 中国体育彩票
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? 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. |
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- 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.?
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?
? 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. |
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- 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.
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.
? 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. |
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- 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.
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.
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? 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?s. |
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- 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.
Participants who complete this course should attain knowledge of data science project life cycle, its technologies and processes.
? Pres?enter(s) | ?Dr Juan Klopper, School for Data Science and Computational Thinking, Stellenbosch 中国体育彩票 |
? 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. |
? Format | ?The course takes place online from 26 - 30 July 2021 The course consists of online videos and synchronous lectures/workshops. |
? 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. |
? Certificate | Attendance certificate from the National Institute of Theoretical and Computational Sciences for those who participate fully. |
? Focus disciplines | ?This course is relevant across all disciplines.? |
?Course outline | Click here? |
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- introduce data and data structures;
- introduce Python programming;
- explore relationship between data using Python.
- basic Python programming;
- understand the benefit of data science;
- basic data science models.
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? Pres?enter(s) | ?Mr Luca Steyn, Department of Statistics and Actuarial Science, Stellenbosch 中国体育彩票 |
? Duration | ?4.5 days from 5 - 9 October 2020 |
??Cost | R1050 including the Microsoft certification vouchers needed to write the formal accreditation examination. |
? Format | ?The course takes place online from 5 – 9 October 2020 1 orientation session + 3 one-hour of Q&A sessions over 5 days
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? Requirements | ?Participants must have a background in computer science, information systems, engineering, and/or data science. |
? Target audience | ?This course is aimed at honours, masters and Phd students, final year students in 4 year degrees, staff, and practitioners. |
? Certificate | ?Certificate of attendance from SU and the opportunity to write the formal Microsoft Azure examination for accreditation through Microsoft. |
? Focus disciplines | ?This course is relevant across all disciplines.? |
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- 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.
- 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?
- A 'voucher' to write the formal Microsoft Azure examination for accreditation through Microsoft.