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​​ 20 - 24 July 2020​​​​

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​
Both online courses run concurrently during the period of 20 to 24 July 2020. Participants are only allowed to register for one. The African Doctoral Academy (ADA) at Stellenbosch University, already well known for their Summer and Winter Doctoral Schools in South Africa and across Africa, will be lending operational support for the school.​​​​​​​
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1 - ​​Introduction to statistics with R
R is statistical software particularly powerful in data analysis and graphical representation. Participants who complete this co​urse should be able to perform basic data manipulation; descriptive​ data analyses including graphical representations; and some basic inferential statistics in R. For further informatio​n click here. 

​2 - Introduction to data science​​
Have you encountered or been expos​ed to data science and want to know more? The short course is designed as an introductory overview to data science explained at the hand of the data science project life cycle. For further information click here. ​



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​​ ​​​Course Information​​​

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1 - ​​Introduction to statistics with R​​​​​
​ Pres​enter(s)
​Mr Hans-Peter Bakker (principal presenter), supported by Emeritus-Professor Niël Le Roux and Professor Sugnet Lubbe from Stellenbosch University’s Department of Statistics and Actuarial Science.
​ Duration
​4.5 days
20 - 24 July 2020. Monday to Thursday 09:00 - 17:00 & Friday 09:00 - 13:00 (a voluntary session on Monday 20th of July 08:00 – 09:00 if students require assistance in installing the necessary software.
​​ Cost
​Early bird: R6 500
Standard rate: R7 500

Early bird registrations end on the 22nd of June 2020. Standard rates apply from the 23rd of June​ 2020.
​ Format
The class will take place in an online classroom with students’ installing the relevant R software and packages on their personal laptops. ​
​ Requirements
General computer literacy as well as some prior knowledge of basic mathematics and statistics are recommended.
​ Target audience
​This course is aimed at anyone interested in strengthening their research and professional capacity in terms of basic statistical understanding and its application using the popular statistical programming software R.​
​ Certificate
​Certificate of attendance
​ Focus disciplines
This course is relevant across all disciplines.​

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General Description
This course offers an introduction in 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.

Course Includes
  • Introduction to the software package R and the user interface RStudio.
  • R Data Structures.
  • Importing and saving data in R.
  • Data manipulation.
  • Writing R functions.
  • R base graphics and introducing the popular graphics package ggplot2.
  • Basic descriptive analyses in R.
  • Applied statistics in R. This will include statistical measures, statistical inference in t-tests and regression analysis; and introducing ANOVA.

Duration: 
5 days: Monday 20 July until Friday 24 July​


2 - Introduction to Data Science​​​​
​ Pres​enter(s)
​Prof. Jacomine Grobler, Dr Christa de Kock and Dr Thorsten Schmidt-Dumont.​
​ Duration
5 days
The course lectures will be pre-recorded and multiple daily online live session will include a catch-up session at 09​:00, a question-and-answer session at 12:00, as well as a wrap-up session at 16:00. These live sessions will be hosted by the lecturers involved in the syllabus taught during that day. These are South African Standard Time.

Cost
Early bird: R7 900
Standard rate: R8 900

Early bird registrations end on the 22nd of June 2020. Standard rates apply from the 23rd of June​​​​​ 2020.
​ Format
​The course lectures will be pre-recorded and multiple daily online live sessions.​
​ Requirements
Bachelor’s degree​
​ Target audience
​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.​
​ Certificate
Certificate of attendance or competence
​ Focus disciplines
This course is relevant across all disciplines.​

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 competence certificate in this short course may allow for exemption of the Data Science (Eng) 774 module which forms part of the Industrial Engineering Postgraduate Diploma in data science.

Outcomes
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 different 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.

Course Includes
The course syllabus will comprise of mainly theoretical elements with the focus on the data science project life cycle. ​
Day 1 - Introduction and data science technologies
Day 2 - Business and data understanding
Day 3 - Data preparation
Day 4 - Modelling methods
Day 5 - Evaluation
Day 5 - Deployment

Duration: ​
5 days: Monday 20 July until Friday 24 July


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