WSQ - Neo4j Graph Data Science and Large Language Model (LLM)

Topic 1: Introduction to Neo4J Graph Data Science

• Overview of Neo4j Graph Data Science (GDS)
• How GDS Works
• Graph Catalog
• Cypher Projections

Topic 2: Graph Algorithms 

• Path Finding
• Community Detection
• Node Embedding
• Similarity
• Shortest Paths with Cypher
• Weighted Shortest Paths

Topic 3: Graph Machine Learning

• Overview of Graph Machine Learning
• Node Classification Pipeline
• Link Prediction
• Exploratory Analysis
• Handling Missing Values
• Encoding Categorical variables
• Dimensionality reduction
• KMeans algorithm
• Feature normalization
• Optimizing KMeans algorithm
• Nearest neighbor graph
• KNN algorithm

Topic 4: Neo4j and LLM 

• Introduction to Neo4j with Generative AI
• Avoiding Hallucination
• Grounding LLMs
• Vectors & Semantic Search
• Vector Indexes
• Introduction to Langchain
• Large Language Models (LLM)
• Chains
• Memory
• Agents
• Retrievers
• Using LLMs for Query Generation
• The Cypher QA Chain
• Conversational Agent

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
• 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.

Minimum Software/Hardware Requirement

Softtware: Windows / Mac

Hardware: Laptop

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

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
• Graph Data Analyst
• Neo4j Developer
• Machine Learning Engineer
• Data Mining Specialist
• AI Research Scientist
• Graph Database Administrator
• Data Analytics Consultant
• Business Intelligence Analyst
• Graph Algorithm Developer
• LLM Application Developer
• AI Solutions Architect
• Data Visualization Expert
• Predictive Analytics Specialist
• Semantic Search Engineer
• Conversational AI Designer
• Natural Language Processing Engineer
• Graph Machine Learning Researcher
• Database Performance Analyst
• Data Strategy Consultant

Yeo Hwee Theng: Yeo Hwee Theng is a data science leader and AI strategist with extensive experience driving enterprise AI adoption and product innovation across healthcare, fintech, and government sectors. As the Data & Analytics Product Lead at Amplify Health, she oversees AI-driven transformation initiatives, designing and implementing advanced data science solutions and enterprise analytics architectures. Her prior roles include AI & Data Architect at Huawei International and Senior Data Scientist at DataRobot, where she delivered large-scale AI deployments across Asia. She holds a Master of Technology in Enterprise Business Analytics from the National University of Singapore and an Advanced Certificate in Learning and Performance (ACLP).

In “Neo4j Graph Data Science and Large Language Model (LLM),” Hwee Theng combines her expertise in data architecture, AI strategy, and applied analytics to help professionals harness graph databases and generative AI for knowledge discovery. Her training emphasizes the integration of Neo4j graph algorithms with LLMs for intelligent data reasoning and context-aware automation. Through practical projects, she equips participants to design scalable graph-based AI systems capable of uncovering deep insights from complex, interconnected data.

Tan Woei Ming: Tan Woei Ming is a data scientist and AI engineer with more than 15 years of experience in machine learning, deep learning, and data automation for the semiconductor industry. Holding a Master’s in Intelligent Systems from NUS and a First-Class Honours in Electrical and Electronic Engineering from NTU, he has led projects in predictive analytics, image recognition, and robotic defect detection at Micron Semiconductor Asia. His work has focused on deploying TensorFlow, PyTorch, and Spark frameworks for large-scale analytics and yield optimization, bridging data science with industrial AI applications.

In “Neo4j Graph Data Science and Large Language Model (LLM),” Woei Ming guides learners through building intelligent graph-based AI systems for industrial and enterprise use cases. His sessions emphasize connecting knowledge graphs with LLMs to enhance reasoning, automation, and decision intelligence. By applying his expertise in data engineering and model deployment, he empowers participants to integrate Neo4j and AI pipelines for advanced analytics and contextual data understanding.

Teh Siew Yee: Teh Siew Yee is a data analytics and digital transformation leader with over two decades of experience across technology, banking, and manufacturing sectors. He has held leadership roles at Standard Chartered, Hewlett-Packard, TikTok, and SIA Engineering, leading teams in AI governance, data management, and business analytics. Siew Yee holds a Master of IT in Business (Artificial Intelligence) from Singapore Management University and a Bachelor of Engineering (Electrical and Electronic) from NTU. A certified ACLP trainer, he is known for delivering engaging, application-driven courses in data science and business intelligence.

In “Neo4j Graph Data Science and Large Language Model (LLM),” Siew Yee teaches professionals how to design and operationalize graph-based AI systems. His training focuses on the convergence of knowledge graphs, NLP, and generative AI—demonstrating how Neo4j can be used to enhance reasoning, contextual retrieval, and intelligent automation. His sessions blend technical rigor with strategic insight, preparing learners to apply graph-based AI in real-world business transformation initiatives.

Truman Ng: Truman Ng is a cloud computing and AI infrastructure specialist with over 20 years of experience in enterprise networking, cybersecurity, and automation. A PMP, ACTA, and Huawei HCIE certified professional, he has trained corporate teams globally in DevOps, AI systems integration, and cloud deployment. His expertise lies in bridging infrastructure and AI engineering, helping organizations build scalable, secure, and data-driven systems for modern enterprises.

In “Neo4j Graph Data Science and Large Language Model (LLM),” Truman teaches how to integrate AI pipelines with Neo4j graph databases in hybrid and cloud environments. His sessions emphasize secure architecture, model orchestration, and performance optimization. By merging practical engineering with AI reasoning concepts, he helps learners design robust, production-ready multi-agent AI and graph-driven solutions that support enterprise-level data intelligence.

James Lee Kin Nam: James Lee is a veteran digital media and IT educator with over two decades of experience in creative technology, automation, and digital transformation. An Adobe Certified Expert and ACLP-qualified instructor, he has trained professionals in digital communication, AI-powered productivity, and information design across universities and corporate programs. His teaching philosophy centers on making advanced technology concepts accessible through visual and experiential learning.

In “Neo4j Graph Data Science and Large Language Model (LLM),” James focuses on the creative and practical aspects of visualizing graph-based data and integrating LLMs into data storytelling. His sessions guide learners to apply Neo4j visualization tools, prompt engineering, and generative models for building intelligent dashboards and narrative-driven AI systems. With his strong design and technology background, he enables participants to communicate complex relationships and insights effectively using graph-based AI.