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African ?Da???ta Science Acade?my??
Introduction to Data and A.I. Ethics? Introduction to Statistics with R? Introduction to Data Science? | ? ? |
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? Pres?enter(s) | ?Department of Philosophy, Stellenbosch 中国体育彩票
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? Duration | 26 - 30 October 2021 |
??Cost | R4,500.00 |
? 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 and AI ethics.? |
? Certificate | Attendance Certificate. |
? Focus disciplines | ?This course is relevant across all data science areas. |
?Course outline | ?Click here?. |
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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 prominent ethical concerns relating to the development, implementation, and use of AI technologies, such as algorithmic bias, responsibility in artificial decision making, AI for good, governance, and best practice.
? The ability to apply ethical theory to practical ethical problems that ?stem from data-related and AI-related practices?.
? Pres?enter(s) | ?Mr Hans-Peter Baker and Prof Sugnet Lubbe |
? Duration | October 3 - December 8 |
??Cost | R6,5?00.00 |
? 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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- 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.
? Pres?enter(s) | ?Prof Jacomine Grobler, Dr Sydney Kasongo, Dr Thorsten Schmidt-Durant |
? Duration | 31 October - 4 November |
??Cost | R7,000.00 for attendance option, R8,500 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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- 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.