JavaScript seems to be disabled in your browser.
You must have JavaScript enabled in your browser to utilize the functionality of this website.

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

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

4 Review(s) | Add Your Review

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.

Learning Outcomes

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.

Course Brochure

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

Skills Framework

This course follows the guideline of Data Mining and Modelling STP-DAT-3003-1.1 TSC under Sea Transport Skills Framework

Certification

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 Funding

WSQ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is subjected to funding caps.

Effective for courses starting from 1 Jan 2024
Full Fee	GST	Nett Fee after Funding (Incl. GST)
		Baseline	MCES / SME
$900	$81.00	$531.00	$351.00

Baseline: Singaporean/PR age 21 and above
MCES(Mid-Career Enhanced Subsidy): S'porean age 40 & above

Upon registration, we will advise further on how to tap on the WSQ Training Subsidy.

You can pay the nett fee (after the WSQ training subsidy) by the following :

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.

Course Code: TGS-2024045802

Post-Course Support

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.

Course Cancellation/Reschedule Policy

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commerce.
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

Final Assessment

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

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

Trainers

Teh Siew Yee: Teh Siew Yee is a seasoned leader in data science and digital transformation, with over 20 years of experience driving organisational strategy, talent development, and the design of data ecosystems across Asia Pacific and global markets. He has successfully led cross-geographical teams and collaborated with industry leaders from the US, UK, China, India, Japan, South Korea, Australia, and beyond, focusing on leveraging data to achieve business objectives and optimize operations.

With expertise spanning predictive modeling, machine learning, deep learning, and IoT, he has hands-on experience in data architecture, engineering, and analytics. He has also developed comprehensive training programs, equipping all levels of an organisation— from C-suite to working-level employees— with the skills needed for digital transformation. His industry experience covers sectors such as tech, education, finance, aerospace, and eCommerce, making him a sought-after expert in data-driven business strategy.

Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners.

Customer Reviews (4)

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 1/14/2019)

Write Your Own Review

You're reviewing: WSQ - Neo4j Graph Data Science and Large Language Model (LLM)

How do you rate this product? *

	1 star	2 stars	3 stars	4 stars	5 stars
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

*Nickname
*Summary of Your Review
*Review
*Please type the letters below
Attention: Captcha is case sensitive.

You May Be Interested In These Courses

WSQ - SQL Fundamental for Beginners

97 Review(s)

$350.00 (GST-exclusive)

$381.50 (GST-inclusive)

WSQ – Mastering NoSQL Fundamentals for Beginners

4 Review(s)

$350.00 (GST-exclusive)

$381.50 (GST-inclusive)

WSQ – Advanced NLP with Large Language Models (LLM)

6 Review(s)

$900.00 (GST-exclusive)

$981.00 (GST-inclusive)

WSQ - AI Application Development with Large Language Models (LLM)

20 Review(s)

$800.00 (GST-exclusive)

$872.00 (GST-inclusive)

WSQ - Neo4j Professional Graph Database Course

14 Review(s)

$800.00 (GST-exclusive)

$872.00 (GST-inclusive)

WSQ - Develop Large Language Model (LLM) Applications with OpenAI API and Langchain

18 Review(s)

$900.00 (GST-exclusive)

$981.00 (GST-inclusive)

About Us

Tertiary Courses, founded in 2014, specializes in providing modular, skill-based courses tailored to Industry 4.0. We are WSQ Approved Training Organization (ATO), and IBF Approved ATO.. We are also Authorized Microsoft Learning Partner, Autodesk Authorized Training Center (ATC), Pearson Vue Authorized Test Center and Kryterion Authorized Testing Center. We offer a comprehensive range of training, certification prep and certification exam services. Additionally, we are a registered supplier with Singapore GeBiz, further solidifying our position as a trusted educational institution in the region.

Training Grants and Subsidies

We have various grants and subsidies for different courses. Please check the respective funding available on individual course page.

WSQ: Company and Singaporeans/PR can apply WSQ 70-90% funding depending on eligibility
SkillsFuture Enterprise Credit (SFEC) - Eligible Singapore-registered companies can tap on $10000 SFEC to cover up to 90% of out-of-pocket expense
SkillsFuture Credit: All Singaporeans aged 25 and above can use their $500 SkillsFuture Credit from the government to pay for a wide range of approved skills-related courses.
IBF-STS Companies and Singaporeans/PR can apply IBF-STS 70-90% funding depending on eligibility
UTAP: NTUC members enjoy 50% subsidy for unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP
SGTech STAR Fund: SGTECH members enjoy $500 grant per pax for SGTech STAR courses