Learning Outcomes;

The Learner will:         

  1. Be able to  extract,  transform  and load (ETL) data.
  2. Be able to explain the ETL testing process and types of ETL testing.
  3. Understand the basic  properties  of matrices and vectors, multiplication,  linear transpose, conjugate, determinant.
  4. Understand inner and outer products, matrix inverse, matrix multiplication rules and algorithms.
  5. Undertake data analysis with R. 
  6. Become familiar structures.
  7. Discover R’s graphic packages.
  8. Be able to create different visualizations using R.
  9. Understand supervised and unsupervised    learning and automated knowledge acquisition.
  10. Be able  able to  analyse  scenarios  for different  industries  using machine learning.                      
  11. Understand design principle for data visualisation.
  12. Be able to explain EDA and perform a   data story presentation and dashboard design for communication.


  • Introduction to Extract,  Transform and Load Process
  • Linear Algebra I          
  • Linear Algebra II         
  • Data  Analysis  with R 
  • Understanding  R Data Structures
  • R’s Graphic packages & Visualisations
  • Automated Knowledge Acquisition
  • Supervised and Unsupervised Learning
  • Analytics Scenarios for Different Industries
  • Machine Learning Models                   
  • Design Principles for    Data Visualisation
  • Exploratory, Descriptive and Diagnostic Analysis
  • Data Story Presentation & Dashboard Design for Communication