Course Details
Course Details
What You'll Learn
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
Assessment
- Written Exam
- Practical Exam
Course Info
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 will need the following for this course:
- A free Microsoft Azure account – a credit card is required for identity verification
Hardware: Windows PC or Mac laptop
Funding Eligibility Criteria
Self-sponsored learners (individuals)
- Singapore Citizens and Singapore Permanent Residents aged 21 and above.
- Up to 50% course fee funding. Singapore Citizens aged 40 and above receive up to 70% under the Mid-career Enhanced Subsidy (MCES).
- Singapore Citizens aged 25 and above can pay the remaining fee with SkillsFuture Credit.
Company-sponsored learners
- Singapore Citizens and Singapore Permanent Residents who are direct employees of the sponsoring company.
- Up to 50% course fee funding, or up to 70% for employees of SMEs (Enhanced Training Support for SMEs) and for Singapore Citizens aged 40 and above. An SME has no more than 200 employees or an annual turnover of no more than $100 million.
- Employers can also claim Absentee Payroll funding for employees who attend during working hours.
- Eligible companies can offset their out-of-pocket fees with SkillsFuture Enterprise Credit (SFEC).
Conditions for all funded learners
- Attendance is taken digitally with Singpass for every session, including the assessment.
- You must complete the course and pass all assessments (assessed as Competent).
- If these conditions are not met, the funding is clawed back and the learner or employer pays the full course fee.
- 9% GST is charged on the full course fee before funding.
- Promo and discount codes cannot be used on funded courses.
- Any balance after funding can be paid in cash, with SkillsFuture Credit, with PSEA (Singapore Citizens with a PSEA), or partly reimbursed by UTAP for NTUC members.
How to Apply SkillsFuture Credit Claim for Individuals
Singapore Citizens aged 25 and above can use their SkillsFuture Credit (base tier) to pay the course fee after funding. The $4,000 mid-career top-up cannot be used for this course.
- Register for this course as self-sponsored and tell us you will use SkillsFuture Credit. Do not pay the amount you plan to claim.
- We email you an invoice showing the course fee after funding.
- From 60 days before the course starts, and no later than the course start date, log in to MySkillsFuture with Singpass and click Claim credits.
- Click Submit a Claim and search for this course by its course code, TGS-2023036642.
- Select your class start date, enter the course fee payable after funding and the amount of credit to claim, then upload the invoice (PDF, JPG or PNG, under 4MB).
- Review, accept the terms and click Agree and Submit. The credit is paid directly to us; processing can take up to 14 working days.
Changing or withdrawing: cancel the claim on MySkillsFuture at least one day before the course starts. If your class date changes, cancel the claim and submit a new one with the new date.
How to Apply SkillsFuture Enterprise Credit (SFEC)
SFEC helps companies pay for employee training. The current SFEC ends on 30 November 2026, and a redesigned SFEC starts on 1 December 2026.
Current SFEC: classes ending on or before 30 November 2026
- Eligible companies received a one-off $10,000 credit, notified by letter to their Corppass administrator. Eligibility generally required at least 3 Singapore Citizen or PR employees and Skills Development Levy contributions.
- SFEC covers up to 90% of the company's out-of-pocket course fee after other subsidies.
- Check your company's SFEC eligibility and balance on EnterpriseSG.
- Register your Singapore Citizen or PR employee for a class that ends by 30 November 2026, as company-sponsored, and pay the course fee after funding.
- Your employee attends and completes the course. We submit the attendance and assessment results to SWDA.
- No separate claim is needed. SFEC is calculated automatically once the course fee grant is approved and is paid quarterly to your company's IRAS GIRO or PayNow Corporate account.
Any unused balance of the current SFEC expires on 30 November 2026.
Redesigned SFEC from 1 December 2026
Companies with at least 3 resident employees receive a fresh $10,000 in an online SFEC wallet that offsets out-of-pocket fees upfront at enrolment, instead of reimbursing them afterwards. SWDA has not yet published the support rates and claim steps; see OurWorkforce or our SFEC guide.
How to Apply UTAP (NTUC Members)
NTUC members can claim 50% of the unfunded course fee under the Union Training Assistance Programme (UTAP), capped at $250 a year (aged below 40) or $500 a year (aged 40 and above). The cap is shared with UTAP claims for AI tool subscriptions.
Eligibility: you must be an NTUC member paying full membership fees, with your membership paid up before and during the course and when you claim. The course must not be fully paid for by your employer or another source.
- Make sure your NTUC membership is paid up before the course starts.
- Register for this course with us and pay the course fee after funding and SkillsFuture Credit.
- Attend at least 75% of the course and sit all assessments.
- Within 6 months after the course ends, go to the NTUC U Portal, open UTAP under E-services, log in with Singpass and choose Course Fee Claim.
- Enter the nett course fee you paid (after all subsidies and SkillsFuture Credit, including GST), and upload your invoice or receipt and your certificate or result slip (PDF, JPG or PNG, up to 5MB each).
- NTUC usually pays the claim within 4 weeks of submission.
Read more in our UTAP guide.
How to Apply PSEA
Singapore Citizens with a Post-Secondary Education Account (PSEA), which stays open until around the year you turn 31, can use it to pay the course fee after funding for PSEA-approved courses. You can also use a sibling's PSEA after your own has been used up.
- Check that this course shows the PSEA tag on its MySkillsFuture page.
- Check your PSEA balance on MOE's Student Finance System with Singpass.
- Download and complete the PSEA withdrawal form, then submit it to us.
- We submit the withdrawal request to MOE. You (and your sibling, if you are using their account) authorise it in the Student Finance System. Nothing is withdrawn without this authorisation.
- MOE pays us directly. If you have already paid, we refund the amount covered by PSEA.
More details: MOE PSEA overview.
Appeal Process
If you disagree with an assessment decision, you have the right to appeal.
- When the assessor gives you feedback, say whether you agree with the outcome. If you agree, you and the assessor sign the Assessment Summary Record; if you disagree, do not sign it.
- Discuss the decision with the assessor or the assessment manager first.
- If you are still not satisfied, tell the assessor you intend to appeal. The assessor records this in the feedback section of the Assessment Summary Record and informs the assessment manager.
- Lodge your appeal within 7 days, giving your reasons, using the Candidate Appeal Form. The assessor can help you write and lodge it.
- An Assessment Appeal Panel reviews the information from you and the assessor and makes a decision. A record of the appeal and its outcome is kept.
- You will be told the outcome within 2 weeks of lodging the appeal. The panel's decision is final.
Job Roles
Job Roles
- 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
Trainers
Trainers
Dr. Alfred Ang is himself a Microsoft Certified Data Scientist (DP-100), so he teaches this certification course as someone who holds the very credential learners are working towards. He is also a Microsoft Certified AI Engineer Associate (AI-102), Power BI Data Analyst (PL-300) and Power App Maker (PL-100), an AWS Certified Solutions Architect, AI Practitioner and Cloud Practitioner, and an IBM certified instructor for the AI Practitioners course. A full-stack AI developer, Agentic AI solution architect and corporate trainer, he is the founder of Tertiary Courses and Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd. His quantitative foundations are strong: a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, where he was top of his cohort, together with more than 20 peer-reviewed papers and more than 20 US patents as co-inventor. He has worked in the defence, electronics and semiconductor industries, and machine learning and deep learning are among his core interests. His day-to-day work spans data engineering, machine learning and cloud-native deployment on platforms such as AWS and Kubernetes. On GitHub, data-mining-ml-labs offers interactive Python and Streamlit labs for beginner data mining and machine learning, and n8n-inventorycontrol shows an AI agent that analyses stock data and computes replenishment forecasts, illustrating how trained models become working business tools.
The WSQ Microsoft Azure Data Scientist Associate (DP-100) course is organised into four learning units that mirror how data science work is done on Azure. In the first, learners explore the Azure Machine Learning workspace and its resources and learn to work with data assets and datastores. The second covers managing data science projects and customising models, including automated machine learning to select the best-performing model for a dataset. The third unit, on running data models and managing capacity, has learners train models with scripts and optimise model training through tuning and efficient use of compute. The final unit turns to model deployment, where learners deploy models and consume them through endpoints so that predictions can be used by real applications. Dr. Ang's background in cloud architecture and DevOps helps learners understand not just how to complete tasks in Azure Machine Learning studio but why compute, environments and pipelines are designed the way they are, which is what the scenario-based exam questions test. He connects each technique to responsible AI practice, drawing on his AI ethics and governance training, and shares the exam preparation strategies that come from earning a long list of Microsoft and AWS certifications. Teaching is hands-on, with learners building, training and deploying their own models in Azure during class. He also explains how to estimate and control compute cost so experiments do not run up unnecessary bills. To view his data science and machine learning projects, see his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
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 modelling and cloud-based data engineering. As co-founder of the Microsoft Business Applications and Power Platform Community in Singapore, he has helped build an ecosystem of professionals advancing cloud and AI adoption, and his background also spans business intelligence, sales analytics, and financial planning and analysis.
In this DP-100 course, Sanjiv equips learners to design, train and deploy machine learning models with Azure Machine Learning. Participants explore the Azure Machine Learning workspace and work with data, automate model selection with automated machine learning, train models with scripts and optimise training, and then deploy and consume models. His training covers the end-to-end workflow, from data preparation and experimentation through to deployment and monitoring. By combining technical expertise with practical case studies, he ensures participants are prepared for the certification and can confidently apply Azure Machine Learning in real business and research contexts.
Quah Chee Yong (QCY) is a WSQ ACLP-certified trainer and data science practitioner with extensive expertise in AI, natural language processing and predictive analytics. He has served as Data Science Training Lead for SAP's SGUnited programme, 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, giving him a practical view of how models are built, tested and put to work.
In Microsoft Azure Data Scientist Associate (DP-100), QCY guides participants through the full Azure Machine Learning workflow, from data ingestion and model training to evaluation and deployment. Learners explore the Azure Machine Learning workspace and work with data, automate model selection with automated machine learning, train models with scripts, optimise model training, and prepare recommendations for model deployment. He emphasises responsible AI principles alongside technical practice, so learners understand both the ethical and operational dimensions of applied AI. His blend of academic and practical expertise enables professionals to apply DP-100 skills effectively in business and research contexts, and to prepare with confidence for the certification.
Alec Tan is a data and cloud professional specialising in Microsoft Azure services, advanced analytics and applied machine learning. 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. 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, and his learner-focused approach emphasises hands-on labs that turn theory into practice.
In Microsoft Azure Data Scientist Associate (DP-100), Alec guides learners through the core skills the certification covers. Participants explore the Azure Machine Learning workspace, work with data, and use automated machine learning to select suitable models. They then train models with scripts, optimise model training and manage compute capacity, before deploying and consuming models. Alec focuses on building ML pipelines, performing model training and deploying AI solutions on Azure, bridging machine learning fundamentals with the Azure cloud ecosystem. Learners leave with practical experience of designing and implementing data science solutions on Azure and a firm foundation for building scalable, production-ready models.
Kishan Raaj is an IT trainer and consultant specialising in Microsoft Azure, data analytics and Python programming. He has extensive experience delivering SkillsFuture-accredited programmes and has trained professionals in cloud computing, business intelligence and AI applications. His ability to simplify complex technical concepts makes him effective in helping learners from diverse backgrounds adopt AI and data science workflows. His teaching combines structured explanations with applied case studies, so participants can connect AI concepts to business outcomes, and his approachable style keeps sessions focused on practical, workplace-ready skills.
In WSQ - Microsoft Azure Data Scientist Associate (DP-100), Kishan emphasises the practical application of Azure Machine Learning. Learners explore the Azure Machine Learning workspace and work with data in it, then automate model selection with automated machine learning. The course moves on to training models with scripts, optimising and managing model runs and capacity, and deploying solutions in cloud environments. Throughout, Kishan helps participants build models, run experiments and interpret the results in a business context. Learners gain certification readiness for DP-100 together with the skills to design and implement data science solutions on Azure and apply Azure Machine Learning confidently in workplace projects.
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