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Data mining techniques are increasingly important for data-intensive manufacturing operations as the industry faces a number of challenges such as equipment and material condition variations, trial-and-error in process parameter setting, product quality inconsistencies, low capability of root cause discovery, process performance prediction and process parameters/recipe auto tuning. By applying data mining techniques, a company can improve its product quality and manufacturing productivity.

This WSQ course aims to provide a good understanding of the fundamentals of data analytics and data mining techniques for different manufacturing applications. Participants will learn techniques for advanced clustering methods for product quality management, correlation modelling, and data pattern methods for root cause analyses and neural networks for process performance prediction.

Why This Course

  • Designed specifically to meet Singapore’s industry demand
  • Highly practical and intensive
  • Latest knowledge and up-to-date technology
  • Case studies highlighting industrial applications
  • Expert trainers in the field with industrial experience

Who Should Attend

This course is designed for operation directors and managers, production/process engineers, R&D engineers and IT support staff working on processes, production and quality improvement in manufacturing industries such as precision engineering, aerospace, automotive electronics, semiconductor, oil and gas, pharmaceutical and medtech.


Watch our webinar video for "Minimising Design of Experiments (DoE) for Process Optimisation Using Data Mining Approach" held on 7 May 2021 for more information about SIMTech's Data Mining Courses.

watch playback

What You Will Learn

What You Will Learn

Fundamentals of Data Mining

  • Introduction to data mining concept and applications in manufacturing
  • Process correlation modelling and data pattern analyses through statistical methods
  • Advanced data clustering technologies for anomaly detection and classification
  • Process performance prediction using artificial intelligence (neural networks, etc)

Case Studies by Grouping Projects Using Real Production Data

Data preparation

  • Problem statement
  • Technical challenges
  • Project objectives
  • Data collection and pre-processing

Data analysis

  • Major factor identification by correlation coefficient analysis
  • Correlation modeling by multiple regression method and root cause analysis
  • K-means clustering and pattern based regression modeling
  • Correlation modeling by fuzzy neural networks method for quality estimation
  • What-if predictive analysis for process improvement & DOE design
  • Project conclusion

Improvement plan

  • Identifying yield improvement areas
  • Production/process improvement plan

download brochure OR register of interest for course

Implement Manufacturing Data Mining Techniques

Please register your interest for this course under 1. Modular Programmes > 2. Implement Manufacturing Data Mining Techniques in the online form.

Note: SIMTech will contact you once registration for the next run of the course you are interested in opens.

Upon Completion of This Course

Upon Completion of this Course

Electronic Statement of Attainment (SOA) certificates will be issued by SkillsFuture Singapore (SSG) to participants who have attended and attained competency in the Singapore Skills Framework training modules. 

Participants must also meet the following criteria to receive their SOAs: 

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

Click here to learn more about the WSQ SOA Certificate.

Statement of Attainment:SSG SOA

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 module is $4,000 before funding and prevailing GST.

Nett Course Fee

Singapore Citizens
(39 yrs old or younger), SPRs or LTVP+ Holders
Singapore Citizens
(40 yrs or older)²
Enhanced Training Support
for SMEs¹
$4,280 $1,284 $484
All fees are inclusive of prevailing GST.

Please note that fees and funding amount are subject to change.

  • Long Term Visit Pass Plus (LTVP+) Holders
    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.

  • 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 more information on the SkillsFuture funding schemes you are eligible for, please visit www.ssg.gov.sg

    How course fees are calculated

    Full Course Fee

    International ParticipantsSingapore Citizens (39 yrs old or younger), SPRs or LTVP+ Holders Singapore Citizens Only
    (40 years old and above) ²
    $4,000  $4,000 SME Sponsored ¹
     $4,000  $4,000
    SkillsFuture Funding Not Applicable  ($2,800)  ($3,600)  ($3,600)
    Total Gross Fee  $4,000 $1,200  $400  $400
    7% GST
     $280 $84*  $84*  $84*
    Total Course Fee Payable to SIMTech $4,280 $1,284  $484  $484
    * Based on 30% of Full Course Fee

    Eligible for SkillsFuture Enterprise Credit (SFEC)

    SkillsFuture Enterprise Credit (SFEC)

    The SkillsFuture Enterprise Credit (SFEC) was first introduced during the Singapore’s Budget 2020 and then recently expanded to a larger group of employers in Budget 2022. It encourages employers to invest in enterprise transformation and capabilities of their employees. Eligible employers will receive a one-off S$10,000 credit to cover up to 90% of out-of-pocket expenses on qualifying costs for supportable initiatives, over and above the support levels of existing schemes.

    Submission of SFEC Claim

    Employers are required to submit their SFEC claims manually via its microsite if they fulfil any of the following scenarios:

    • Employers have sent their foreign employees (excluding foreign employees who hold Long-Term Visit Pass Plus) for training on a SFEC-eligible course;

    Employers can only submit the claims for SFEC after their employees have completed the training course(s). Click here to download the SFEC Claim e-guide.
    NOTE: You will require a Corppass account with "EPJS_User" role assigned prior to login to your account to manage your claims.

    If you are unsure whether your company is eligible for SFEC, you may submit an enquiry to Enterprise Singapore (ESG) via here for their response.

    Contact Us

    download brochure OR register of interest for course

    Implement Manufacturing Data Mining Techniques

    Please register your interest for this course under 1. Modular Programmes > 2. Implement Manufacturing Data Mining Techniques in the online form.


    Skills Course Reference Number Training Period
    (Click on dates to view schedules)
    Registration Status
    Implement Manufacturing Data Mining Techniques (42 hours) TGS-2020506158

    Data Mining for Correlation Analysis (DM-LITE) TGS-2020504604    
    : Full Day  AM
    : Morning PM
    : Afternoon EVE
    : Evening