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Enhance Your Employability with Certified Skills and Courses in Singapore - WSQ , IBF-STS, Skills Certification

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

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:

  • Develop Neo4j graph data science guidelines to enhance data-mining applications.
  • Identify and rectify data problems using graph database algorithms.
  • Construct graph machine learning models to identify patterns and trends in data sets.
  • 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

Course Booking

The course fee listed below is before subsidy/grant, if applicable. We will apply for the grant and send you the invoice with nett fee.

$900.00 (GST-exclusive)
$981.00 (GST-inclusive)

Course Date

* Required Fields

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 and get back to you asap.

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


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 

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
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. (Posted on 10/29/2024)
will recommend Review by Course Participant/Trainee
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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
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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)

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