Data Mining for Correlation Analysis (DM-LITE)

Data Mining for Correlation Analysis (DM-LITE)


Upcoming Course Date


Training Duration

16 Hrs

Full Fee


Funding Support

Up to 90%

Training Mode

Classroom Based, Hands-on


Entry Level, Aspiring Professional

Data Mining for Correlation Analysis (DM-LITE)


This course aims to provide participants with up-to-date technologies in data mining. Through extensive hands-on and sharing of successful case studies, it allows the participants to have the confidence and ability to use data mining techniques to help them in their daily work. 

The course will provide the participants with a set of methodology for conducting problem-solving using data mining. From the basics of methodologies in data collection, pre-process data from multiple sources, cleaning of the data, to finally using data mining techniques to analyse the data and solve actual industrial problems. 

Learning Objectives: 

  • Understand the fundamentals of data mining technologies 
  • Gain knowledge of actual industry case studies of how data mining can be used to solve actual industrial problems 
  • Understand data collection and pre-process methodologies 
  • Understand the K-means clustering method and its application 
  • Able to apply correlation analysis to identify the major factors for root cause analysis 
  • Understand and apply predictive modelling by multiple regression and neural networks
  • Apply smart design of experiment with What-If analysis through predictive models





International Participants

Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders

Employer-sponsored and self-sponsored Singapore Citizens aged 40 years and above (MCES)

SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders) (ETSS)






Singaporeans aged 25 years old and above are eligible for SkillsFuture Credit which can be used to offset course fees (for self-sponsored registrations only)

For corporate training or individual participants, please let us know your preferred date(s) in the Register Interest form below.

Related Courses