WSQ - Microsoft Azure Data Scientist Associate (DP-100)

LU1 Work with Data using Azure Machine Learning

• Topic 1 Explore the Azure Machine Learning workspace
• Topic 2 Work with data in Azure Machine Learning

LU2 Manage Data Science Projects and Customize Data Models

• Topic 3 Automate machine learning model selection with Azure Machine Learning

LU3 Run Data Model and Manage Capacity

• Topic 4 Train models with scripts in Azure Machine Learning
• Topic 5 Optimize model training in Azure Machine Learning

LU4 Recommendations for Model Deployment

• Topic 6 Deploy and consume models with Azure Machine Learning

Final Assessment

• Written Assessment - Short Answer Questions (WA-SAQ)
• Practical Performance (PP)

Promotion Code

Promo or discount cannot be applied to WSQ courses

Minimum Entry Requirement

Knowledge and Skills

• Able to operate using computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• 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 Year Group : 21-65 years old

Minimum Software/Hardware Requirement

Software:

You need to sign up a Azure account (Credit Card is required).

Hardware: Windows and Mac Laptops

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Steps to Apply Skills Future Claim

• The staff will send you an invoice with the fee breakdown.
• Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
• Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
• Upload your invoice and click ‘Submit’

SkillsFuture Level-Up Program

The  SkillsFuture Level-Up Programme provides greater structural support for mid-career Singaporeans aged 40 years and above to pursue a substantive skills reboot and stay relevant in a changing economy. For more information, visit SkillsFuture Level-Up Programme

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

• Log in to your U Portal account to submit your UTAP application upon completion of the course.

Note

• SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
• All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
• An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
• UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
• UTAP support amount will be paid to training provider first and claimed after end of class by learner.

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.

• Data Scientist
• Azure Data Engineer
• Azure Solution Architect
• Machine Learning Engineer
• Cloud Data Scientist
• Data Analytics Manager
• Cloud Solution Consultant
• Azure DevOps Engineer
• AI Developer on Azure
• Data Science Consultant
• Cloud Infrastructure Specialist
• Big Data Engineer on Azure
• Data Platform Specialist
• Machine Learning Operations (MLOps) Engineer
• Cloud Application Developer

Sanjiv Venkatram - Sanjiv Venkatram is a Microsoft Most Valuable Professional (MVP), Microsoft Certified Trainer (MCT), and CEO of Prudentia Consulting with more than 20 years of experience in digital transformation and applied AI across North America and Asia-Pacific. He has delivered enterprise-grade AI and data solutions on Microsoft Azure, including machine learning pipelines, predictive modeling, and cloud-based data engineering. As co-founder of the Microsoft Business Applications and Power Platform Community in Singapore, he has helped build a strong ecosystem of professionals advancing cloud and AI adoption.

In DP-100 training, Sanjiv equips learners with the skills to design, train, and deploy machine learning models using Azure Machine Learning. His training covers end-to-end workflows, from data preparation and experimentation to deployment and monitoring. By combining technical expertise with practical case studies, he ensures participants can confidently apply Azure ML in real-world business and research contexts.

Alec Tan - Alec Tan is a data and cloud professional specializing in Microsoft Azure services, advanced analytics, and applied machine learning. With extensive training experience in data fundamentals, SQL, and cloud-based AI solutions, he has supported professionals across industries in adopting Azure for modern data science workflows. His expertise spans Azure SQL, Cosmos DB, and Azure Machine Learning Studio, giving him both breadth and depth across the Azure data and AI stack.

In this program, Alec focuses on guiding learners through DP-100 core skills, including building ML pipelines, performing model training, and deploying AI solutions on Azure. His learner-focused approach emphasizes hands-on labs, ensuring participants can translate theory into practice. By bridging machine learning fundamentals with Azure’s cloud ecosystem, Alec empowers learners to build scalable and production-ready AI models.

Kishan Raaj - Kishan Raaj is an IT trainer and consultant specializing in Microsoft Azure, data analytics, and Python programming. With extensive experience delivering SkillsFuture-accredited programs, he has trained professionals in cloud computing, business intelligence, and AI applications. His ability to simplify complex technical concepts makes him highly effective in equipping learners from diverse backgrounds to adopt AI and data science workflows.

In DP-100 training, Kishan emphasizes practical application of Azure Machine Learning, helping learners build models, run experiments, and deploy solutions in cloud environments. His teaching combines structured explanations with applied case studies, enabling participants to connect AI concepts with business outcomes. His approachable style ensures learners not only gain certification readiness but also the skills to apply Azure ML confidently in workplace projects.

Quah Chee Yong (QCY) - Quah Chee Yong (QCY) is a WSQ ACLP-certified trainer and data science practitioner with extensive expertise in AI, NLP, and predictive analytics. He has served as Data Science Training Lead for SAP’s SGUnited program, designing and delivering training in machine learning, R, Python, and AI adoption. His industry experience includes building digital assistants, recommender systems, and data-driven business solutions that leverage machine learning pipelines and knowledge representation.

In this course, QCY guides participants through the full Azure Machine Learning workflow, from data ingestion and model training to evaluation and deployment. He emphasizes responsible AI principles alongside technical practices, ensuring learners understand both the ethical and operational dimensions of applied AI. His blend of academic and practical expertise enables professionals to harness DP-100 skills effectively in business and research contexts.

Truman Ng - Truman Ng is a highly certified ICT and infrastructure professional with more than 20 years of experience in networking, cybersecurity, and enterprise systems. He holds certifications including PMP®, HCIE®, and ACTA, and has trained professionals in Linux, DevOps, RPA, Docker, and cloud computing. With hands-on expertise in IT infrastructure, automation, and data workflows, he brings a strong applied perspective to cloud-based AI adoption.

In Azure Data Scientist Associate training, Truman focuses on integrating AI model development with enterprise IT environments. He guides learners through deploying Azure ML models securely and efficiently, ensuring that they can scale solutions in organizational contexts. His practical, infrastructure-driven approach complements data science workflows, giving participants the skills to manage AI projects from both technical and operational perspectives.