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

Sessions 2 days
Duration 16 hrs
Level Intermediate
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.

TSC Title: Data Mining and Modelling
TSC Code: STP-DAT-3003-1.1 TSC

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

LO1: Develop Neo4j graph data science guidelines to enhance data-mining applications.
LO2: Identify and rectify data problems using graph database algorithms.
LO3: Construct graph machine learning models to identify patterns and trends in data sets.
LO4: Perform narrative analytics using Large Language Model (LLM) models on Neo4j graph data sets.

Download Course Brochure

Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.
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 - Neo4j Graph Data Science and Large Language Model (LLM)

Course Code: TGS-2024045802

WSQ
SkillsFuture Credit
PSEA
UTAP
SFEC
Absentee Payroll
MCES

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This comprehensive course is designed to equip participants with the skills to leverage Neo4j Graph Data Science (GDS) and Large Language Model (LLM) technologies to enhance data-mining applications and resolve complex data challenges. Beginning with an introduction to Neo4j GDS, learners will gain an understanding of how GDS operates, including its Graph Catalog and Cypher Projections, setting a solid foundation for exploring advanced graph algorithms. These include pathfinding, community detection, node embedding, similarity analysis, and the application of weighted shortest paths for intricate data analysis.

Building on this knowledge, the course delves into Graph Machine Learning, covering essential techniques such as node classification, link prediction, and exploratory analysis. Participants will learn how to handle missing values, encode categorical variables, and implement feature normalization, with a focus on optimizing the KMeans algorithm and nearest neighbor graphs. The final segment explores the integration of Neo4j with Large Language Models (LLM), including techniques to avoid hallucination, grounding LLMs, and utilizing LLMs for query generation and narrative analytics. By the end of this course, learners will be equipped to construct graph machine learning models and perform narrative analytics using LLM models on Neo4j graph datasets, positioning them at the forefront of data science innovation.

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.Click here to submit SkillsFuture Enterprise Credit

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. Click here for SkillsFuture Credit submission

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. Click here to submit UTAP

PSEA

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

To check for Post-Secondary Education Account (PSEA) eligibility for this course, Visit SkillsFuture (course code: TGS-2024045802)

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 and email to us.

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

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.

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

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

Written Exam
Practical Exam

Course Info

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

Individual Sponsored Trainee

Employer Sponsored Trainee

Singapore Citizens or Singapore Permanent Residents aged 21 and above
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.

Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.
Trainee must pass all prescribed tests / assessments and attain 100% competency.
We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.

SkillsFuture Credit:

Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

PSEA:

To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
Scroll down to "Keyword Tags" to verify for PSEA eligibility.
If there is “PSEA” under keyword tags, the course is eligible for PSEA.
And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA.
Not all courses are eligible for PSEA funding.

Absentee Payroll (AP) Funding:

$4.50 per hour, capped at $100,000 per enterprise per calendar year.
AP funding will be computed based on the actual number of training hours attended by the trainee.

SFEC:

If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SSG would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
SkillsFuture Enterprise Credit (SFEC) Microsite

Appeal Process

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

Job Roles

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

Review

Customer Reviews (5)

will recommend 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

Some parts of the slides shared were outdated. Perhaps could update them so for future participants (Posted on 5/9/2025)

will recommend 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

. (Posted on 10/29/2024)

will recommend 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

. (Posted on 10/29/2024)

Will Recommend 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

. (Posted on 3/31/2019)

Will Recommend 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

Provide detailed training notes including the steps , in addition to training notes , together with sample codes as tutorials

nstead of one full Sunday, which is difficult to absorb, split to 4 afternoons/4 mornings on weekends .Better chance for student to absorb and practice. (Posted on 1/13/2019)