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WSQ - Practical Design of Experiment (DoE) for Engineers and Researchers

Course Information

  • Sessions 2 days
  • Duration 16 hrs
  • Level Beginner
  • Assessment 2 hrs

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Skills Framework

TSC Title
Process Integration
TSC Code
ELE-SIS-5002-1.1

Funding Validity

Funding for this course is valid from 20 Nov 2024 to 19 Nov 2026. Register and complete the course within this period to qualify for funding support.

Learning Outcomes

By end of the course, learners should be able to:

  • LO1: Analyze process interactions to characterize key factors influencing performance in Design of Experiments (DoE).
  • LO2: Select suitable factorial DoE projects by evaluating relevant performance metrics.
  • LO3: Define the scope and execute fractional factorial DoE projects using problem-solving techniques.
  • LO4: Evaluate the effectiveness of DoE projects and recommend follow-up actions

Course Brochure

Download Course Brochure

Certification

  • Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.
  • OpenCerts from SkillsFuture Singapore - After passing the assessment(s) and achieving at least 75% attendance, participants will receive a OpenCert (aka Statement of Achievement) from SkillsFuture Singapore, certifying that they have achieved the Competency Standard(s) in the above Skills Framework.

WSQ - Practical Design of Experiment (DoE) for Engineers and Researchers

Course Code: TGS-2024051249
  • WSQ
  • SFC
  • PSEA
  • UTAP
  • SFEC
  • Absentee Payroll
  • MCES

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What's This Course About

This WSQ course, Practical Design of Experiment (DoE) for Engineers and Researchers, equips participants with essential skills to design, analyze, and optimize experiments for improved process performance. Participants will gain a thorough understanding of DoE fundamentals, factorial experiments, and how to apply ANOVA to assess the significance of variables. By learning how to identify key factors affecting performance, participants will be able to confidently select appropriate DoE projects and execute them with precision.

The course covers advanced topics like fractional factorial designs, screening methods, and modeling techniques such as Taguchi and Response Surface Methodology (RSM). By the end of the course, learners will be able to evaluate the effectiveness of their DoE projects and make data-driven recommendations for continuous process improvement.

WSQ Funding

Full Fee $900.00 Before GST
GST $81.00 9% of fee
Baseline Nett $531.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $351.00 SG age 40+ · 70% funded
SkillsFuture Enterprise Credit (SFEC)

Eligible Singapore-registered companies can tap on $10000 SFEC to cover out-of-pocket expenses.

View on SkillsFuture for Business

SkillsFuture Credit (SFC)

Eligible Singapore Citizens can use their SFC to offset course fee payable after funding but the $4,000 Additional SFC (Mid-Career Support) cannot be used.

Direct Application on SkillsFuture Portal

UTAP

Eligible NTUC members can apply for 50% of the unfunded fee from UTAP, capped up to $250/year and for members aged 40 and above, capped up to $500/year.

Submit UTAP Claim

PSEA

Eligible Singapore Citizens can use their PSEA funds to offset course fee payable after funding.

Check PSEA Eligibility

  • Scroll down to “Keyword Tags” to verify for PSEA eligibility.
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.

Once you are eligible for PSEA, please download and fill up the PSEA Withdrawal Form, then submit the completed form to us:

PSEA Withdrawal FormSubmit PSEA Form

Course FeeBefore Funding

$900.00 (GST-exclusive)
$981.00 (GST-inclusive)

Course Date

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Fundamentals of Design of Experiment 

Introduction to Design of Experiment (DoE)

Dependent and Independent variables

Purpose of DoE

Stages of DoE

Factor, Level and Treatment

Introduction to single factor experiments

One-Way Analysis of Variance (ANOVA)

Decomposition of the Sum of Squares

Topic 2 Factorial DoE

Introduction to Factorial DoE

Main Effects and Interactions between factors

Why using Factorial DoE

Two-Factors Two-Levels (2^2) DoE

Regression equation for 2^2 DoE

2^2 experiment with Interactions

Regression model for 2^2 DoE with Interactions

Analysis of Variance (ANOVA) of 2^2 DoE

Adding the third factor – 2^3 DoE

ANOVA of 2^3 DoE

Regression model for 2^3 DoE

General 2^k DoE

Analysis procedure of any 2^k DoE

Blocking a replicated design

Analysis a 2^k DoE with blocks as replicates

Confounding a 2^k DoE in blocks

Topic 3 Fractional Factorial DoE 

Introduction to Fractional Factorial DoE

One-Half fraction designs

Confounding in partial factorial design

Design resolution

ANOVA of fractional DoE

One-Quarter fraction designs

Topic 4 Screening, Modeling and Optimizing DoE

Screening designs

Plackett Burman design

Taguchi design

Response Surface Method (RSM)

Central Composite Design (CCD)

Assessment

  • Written Exam
  • Practical Exam

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

Hardware: Window or Mac Laptops

Appeal Process

  1. The candidate has the right to disagree with the assessment decision made by the assessor.
  2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.
  3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.
  4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.
  5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.
  6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.
  7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.
  8. The candidate must lodge the appeal within 7 days, giving reasons for appeal 
  9. The assessor can help the candidate with writing and lodging the appeal.
  10. he assessment manager will collect information from the candidate & assessor and give a final decision.
  11. A record of the appeal and any subsequent actions and findings will be made.
  12. An Assessment Appeal Panel will be formed to review and give a decision.
  13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.
  14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.
  15. Please click the link below to fill up the Candidates Appeal Form.

Job Roles

Job Roles

  • Process Engineer
  • Quality Assurance Engineer
  • Product Development Engineer
  • Manufacturing Engineer
  • Research Scientist
  • Data Analyst
  • Operations Manager
  • Continuous Improvement Manager
  • Industrial Engineer
  • Test Engineer
  • R&D Manager
  • Laboratory Technician
  • Engineering Consultant
  • Process Improvement Specialist
  • Lean Six Sigma Specialist
  • Project Manager
  • Production Manager
  • Quality Control Specialist
  • Statistical Analyst
  • Technical Consultant

Trainers

Trainers

Dr. Alvin Ang is a data analytics and digital transformation expert with more than 20 years of experience in applied research, engineering innovation, and data-driven decision-making. He holds a PhD in Information Systems and has worked with numerous public and private sector organizations to implement analytical frameworks that enhance operational efficiency and quality management. His expertise spans statistical modeling, process optimization, and experimental design, making him a sought-after consultant and trainer in the fields of engineering analytics and applied research.
In this course, Dr. Ang focuses on translating complex experimental design methodologies into practical applications for engineers and researchers. His sessions emphasize structured problem-solving, data validation, and statistical inference using real-world case studies. Learners benefit from his deep expertise in process optimization and his ability to connect data science with experimental design for improved innovation and research outcomes.

Teddy Yip Fook Khin is a senior technology consultant and educator with extensive experience in IT systems, AI integration, and data analytics. Over the past 20 years, he has led projects in data modeling, statistical analysis, and process improvement for industries including manufacturing, engineering, and business services. As a certified ACLP trainer, Teddy combines technical depth with instructional clarity, guiding learners to master analytical techniques for problem-solving and continuous improvement.
In this course, Teddy introduces participants to the principles of Design of Experiments and their role in enhancing product quality and process control. His sessions focus on applying statistical design methods to real-world engineering challenges using tools such as Minitab and Python. Learners gain practical insights into experimental setup, analysis, and interpretation, equipping them to drive data-based optimization in research and engineering environments.

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.

Liew Sing Loon is an experienced educator and professional engineer with a strong background in applied research, process design, and learning development. He holds the

from the Institute for Adult Learning (IAL) and has over 15 years of experience in technical training, curriculum design, and engineering innovation. His expertise spans across process optimization, problem-solving methodologies, and technology-enabled learning, making him a valuable resource for engineers and researchers seeking structured analytical skills.
In this course, Liew focuses on helping participants understand the practical application of Design of Experiments for improving system performance and reliability. His sessions integrate real-world engineering examples with structured experimental design concepts to ensure learners can apply techniques effectively in research and production settings. Learners gain hands-on experience in planning, executing, and analyzing experiments that enhance innovation and data-driven decision-making.

Review

Customer Reviews (3)

Average Rating: 5.0 Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
N/A (Posted on 5/22/2026)
Average Rating: 3.7 Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Classrooms is hot in the afternoon and was not clean, very dusty (Posted on 5/21/2026)
Average Rating: 3.7/5 Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
N/A (Posted on 3/12/2026)

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