Data Mining for Correlation Analysis
*This is a non-WSQ module.


Data mining is a technique used to extract useful information from a large number of datasets. A good understanding of the updated data mining techniques and the ability to use effectively is increasingly important for data-intensive manufacturing operations. This training is targeted towards manufacturing companies and companies in other industries which are data rich but information poor. It aims to provide the trainees understanding of up-to-date data mining technologies, build a fundamental about data analytics and data mining techniques for various applications such as process optimisation, correlation analysis for major factor identification, product quality improvement and many more.

about the programme

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.

At the end of the course, the participants can:

  • 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

Who Should Attend

This course is designed for professionals such as engineers, managers, researchers, IT support staff as well as management in semiconductor, electronics, precision engineering, aerospace, automation, medtech, pharmaceutical and logistic industries.

Course Outline

Course Outline

The programme consists of 4 learning stages.

Stage 1
Trainees will learn up-to-date data mining technologies through an overview of data mining introduction. They will also gain knowledge of the successful applications of data mining in local industry companies through case-studies sharing.

Stage 2
Trainees will learn to carry out data collection and data pre-processing. They will also learn how to use K-means clustering to discover anomaly data patterns. They will have an opportunity to use data mining software to carry out hands-on sessions to understand the k-means clustering method.

Stage 3
Trainees will learn about correlation analysis for major factor identification. They will also learn the fundamental of predictive modelling by multiple regression. A smart design of experiment (DoE) with what-if analysis will be carried out based on a predictive model. For each of the learning point, trainees will be given an opportunity to try and apply what they learned through hands-on sessions.

Stage 4
Trainees will learn about Artificial Intelligence (AI) methods. The basics of Neural Networks (NN) and Fuzzy Neural Networks (FNN) will be introduced. The trainees will learn how to apply NN and FNN for performance prediction through hands-on sessions. They will also have an opportunity to compare the advantage and disadvantage of predictive modelling methods through a case study. The trainees will gain a solid foundation and apply what they have learned.

Upon Completion of This Course


Participants will be awarded with a Certificate of Attendance (COA) by SIMTech and/or ARTC if they meet the following criteria:

  • Achieve at least 75% course attendance;
  • Take all assessments; and
  • Pass the course.

Note: Trainees will have to bear the full courses fee upon failure to meet either one of the criteria.

Pre-Requisites, Full and Nett Course Fees


  • Applicants should possess a degree in any discipline or a diploma with a minimum of 3 years of related working experience.
  • Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.
  • Proficiency in written and spoken English.

Full Course Fee

The full course fee for this course is $1,600 before funding and prevailing GST.

Nett Course Fee

Singapore Citizens aged 39 years and below, Singapore Permanent Residents and LTVP+ Holders
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years and above (MCES) ²SME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders) (ETSS) ¹
All fees are inclusive of GST 9%.
Please note that fees and funding amount are subject to change.


  • The LTVP+ scheme applies to lawful foreign spouses of Singapore Citizens with (i) at least one Singapore Citizen child or are expecting one from the marriage, or at least three years of marriage, and (ii) where the Singapore Citizen sponsor is able to support the family.
  •  All LTVP+ holders can be identified with their green visit pass cards, with the word ‘PLUS’ printed on the back of the card.


SMEs that meet all of the following eligibility criteria:

  • Registered or incorporated in Singapore
  • Employment size of not more than 200 or with annual sales turnover of not more than $100 million

SME-sponsored Trainees:

  • Must be Singapore Citizens or Singapore Permanent Residents.
  • Courses have to be fully paid for by the employer.
  • Trainee is not a full-time national serviceman. 

Further Info: This scheme is intended for all organisations, including non-business entities not registered with ACRA e.g. VWOs, societies, etc. Only ministries, statutory boards, and other government agencies are NOT eligible under Enhanced Training Support for SMEs Scheme. Sole proprietorships which meet all of the above criteria are also eligible.


SkillsFuture Credit

For more information on the SkillsFuture funding schemes you are eligible for, please visit

How course fees are calculated

International Participants Singapore Citizens aged 39 years and below, Singapore Permanent Residents and LTVP+ HoldersEmployer-sponsored and self-sponsored Singapore Citizens aged 40 years and aboveSME-sponsored local employees (i.e Singapore Citizens, Singapore Permanent Residents and LTVP+ Holders)
 Not applicable for SkillsFuture FundingSkillsFuture Funding (Baseline)SkillsFuture Mid-career Enhanced Subsidy (MCES) ²SkillsFuture Enhanced Training Support for SMEs (ETSS) ¹
Full Course Fee$1,600 $1,600 $1,600 $1,600 
SkillsFuture FundingNot Applicable  ($1,120)($1,440)($1,440)
Nett Course Fee$1,600$480 $160$160
GST 9%
$144 $43.20* $43.20* $43.20*
Total Nett Course Fee Payable to Training Provider$1,744$523.20 $203.20 $203.20
* Based on 30% of Full Course Fee

Contact Us

  • For technical enquiries, please contact:

Ms GE Hailin,

  • For general enquiries, please contact:

Knowledge Transfer Office,


  • Please register for this course through our Course Registration Form for Public Classes.
  • For the first question, please select Qn1: Modular Programmes
  • Applicants will be placed on our waiting list if the course does not have an upcoming scheduled intake.
  • Once the next intake is confirmed to commence, SIMTech will contact the applicants to share the class information.


Skills Course Reference NumberTraining Period
(Click on dates to view schedules)
Registration Status
  • Data Mining for Correlation Analysis (DM-LITE) (16 hours)
TGS-2020504604The next intake's schedule is still in the planning stage. 

Note: SIMTech and ARTC reserve the right to change the class/schedule/course fee or any details about the course without prior notice to the participants.


  • From 1 Oct 2023, attendance-taking for SkillsFuture Singapore (SSG)'s funded courses must be done digitally via the Singpass App. More information may be viewed here.
FD: Full day
AM: Morning
PM: Afternoon
EVE: Evening