People Innovation Excellence


Learning Outcomes:

On successful completion of this Course, students will be able to: apply core theory of selected topics in industrial engineering disciplines; differentiate selected topics in industrial engineering disciplines in different settings and case studies; analyse the most appropriate methods among selected topics in industrial engineering disciplines; apply the most appropriate methods to solve real engineering problems.


  1. An introduction to data science;
  2. Data and pre-processing data;
  3. Data dimensionality reductions;
  4. Frequent itemset mining and association rule mining: An introduction;
  5. Classification technique using Decision tree and Bayes classification;
  6. Classification: advanced method using BBN and SVM;
  7. Classification using Artificial Neural Nework (ANN);
  8. Clustering: Basic concept using K Means;
  9. Advance method in clustering;
  10. Adaptive Neuro Fuzzy Inference System (ANFIS);
  11. Enrichment Activity: Discussion – Data and pre-processing data using R programming (Enrichment);
  12. Enrichment Activity: Lab – Advance frequent itemset mining and association rule mining (Enrichment);
  13. Enrichment Activity: GUEST LECTURE – Business analytics using data science techniques and case study;
  14. Enrichment Activity: Consultation – Classification: advanced method; Enrichment Activity: Discussion – Clustering for high dimensional data using R programming;
  15. Enrichment Activity: GUEST LECTURE – Data science trend and application and opportunity for research frontiers.

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