Note due to the impact of Covid-19, this program will be divided into 4-modules but the first 3 modules will be done online and the final module will be a workshop hosted in Cape Town in May 2022 (if travel restrictions have been lifted, if not, this will be converted to a an online module). Participants who successfully complete the first three modules will be eligible to attend the final workshop.

Each module will tackle a different real-world challenge and will allow participants to develop new technical skills pertinent to that project. All modules will enhance participant’s general data science readiness, project management, communication and leadership skills.

 

Module 1 Module 2 Module 3 Module 4
Platform: Online

Duration: 3 weeks
(25-30 hours/week)

Provisional Dates:
24 January – 11 February 2022

 

 Platform: Online

Duration: 3 weeks
(25-30 hours/week)

Provisional Dates:
21 February – 11 March 2022

 Platform: Online

Duration: 3 weeks
(25-30 hours/week) 

Provisional Dates:
21 March – 8 April 2022

 Platform: Online

Duration: 3 weeks

Provisional Dates:
9 – 27 May 2022

or

Platform: Workshop

Duration: TBC

Provisional Dates: May 2022

NOTE: participants will be expected to work on their final projects in the break between Module 3 and 4.

The goals of the program are to deliver:

  • Hands-on experience and deep understanding of key technical skills in data science including machine learning, big data computing and business intelligence.
  • A dynamic network of colleagues who will be future leaders and data scientists.
  • A portfolio of real-world success stories in data science, backed up by quantitative performance metrics.
  • The ability to rapidly learn new data science skills to allow them to remain on the cutting edge despite the rapidly evolution in the field.
  • An understanding of which areas of data science they wish to specialise in.

 

Among many others, the program will cover:

    • Advanced python skills
    • Managing the data science project life cycle
    • Problem solving and model selection: starting to build a solution
    • Machine learning algorithms: which one is right?
    • Deep learning and neural networks for AI based on Google’s Tensorflow
    • Big data computing including Spark and related database technologies
    • Natural language processing and machine learning for text
    • Testing and quality assurance for data science projects
    • Scrum as a methology for agile project management
    • Productionalising data science projects with cloud services such as AWS
    • Creating stunning visualisations and dashboards and compelling story-telling.