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

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)

Final Assesment

• Written Assessment (Short Answer Questions)
• Case Study

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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:

• Intellij  https://www.jetbrains.com/idea/
  

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.

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Dr. Alvin Ang: 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: 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: Dwight Nuwan Fonseka is a data scientist and analytics leader with extensive experience in data-driven experimentation, predictive modeling, and applied research. As Head of Data Science at Plano Pte Ltd, he has led projects involving machine learning, optimization, and algorithmic experimentation. Dwight’s strong foundation in statistical design and computational modeling enables him to guide learners in using data to improve innovation and operational excellence.

In this course, Dwight teaches the use of statistical design and analysis techniques for optimizing experiments in engineering and scientific contexts. His sessions emphasize the application of AI and data analytics in modern DoE workflows, helping learners build robust models for process improvement and hypothesis testing. Participants gain a solid foundation in both classical and computational approaches to experimental design and analysis.

Liew Sing Loon: 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 Advanced Certificate in Learning and Performance (ACLP) 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.