NUS
 
ISS
 
NUS ISS

Data Engineering for Analytics

Build reliable data pipelines and trusted data ecosystems that help organisations scale analytics and AI

Overview

Part of Graduate Certificate in Predictive Analytics Lifecycle
Duration 3 days
Course Time
Enquiry Please contact ask-iss@nus.edu.sg for more details.

Organisations have dashboards, analytics teams and AI ambitions, but still struggle to scale them across the enterprise. Data sits in separate systems, business metrics are defined differently across teams, and pipelines are often rebuilt project by project. This leads to duplicated work, inconsistent reporting, slow delivery and fragile foundations for AI.

Data Engineering for Analytics is a 3-day intermediate course that helps you design the data pipelines, models and ecosystems needed to make analytics and AI repeatable, reliable and scalable. You will learn how to build dimensional data models, ETL/ELT pipelines, workflow orchestration, SQL and NoSQL solutions, and modern data architectures that turn enterprise data into trusted, reusable data assets. It connects operational systems, data warehouses, and analytics platforms, enabling scalable data-driven decision-making. As such, data engineering is a core discipline in modern data, AI, and analytics ecosystems.

The course focuses on the engineering layer that connects operational systems to business intelligence, advanced analytics, machine learning and AI applications. You will also explore how generative AI and AI-assisted tools can support schema design, pipeline development, transformation logic and synthetic data generation.

By the end of the course, you will be better equipped to reduce duplicated data work, improve consistency in reporting, accelerate analytics delivery and support stronger foundations for enterprise AI.

 

Upcoming Classes

Class 1: 24 to 27 November 2026 
Duration: 3 Days
When: Nov 24, 26, 27
Mode of Conduct: Face-to-Face
Time: 9:00am to 5:00pm

 

Register

 

Registration Instructions

Self-sponsored Participants

  • Register for the course by clicking on the "Register Now" button above
  • You may refer to the User Guide for Learner

Company-sponsored Participants

  • You will have to be registered for the course by someone from your company who has an account on the LifeLong Learning Portal (L³AP)
  • The person in-charge may register you for the course by:
    • Generating a corporate registration link for you to register for the course
      • After the link is generated, you must:
        1. Log in to L³AP by clicking on the "Register Now" button
        2. Click on the corporate registration link after logging in
      • If you do not follow the above instructions, you will be registered for the course as self-sponsored
    • Registering you for the course backend
      • You will still be required to log in to L³AP and complete your registration by clicking on the "Register Now" button above
  • You may refer your HR/L&D POC to the User Guide for Company

Key Takeaways

At the end of the course, participants are expected to be able to:

  • Design dimensional data models for analytics and business intelligence so that reporting, dashboards, and OLAP workloads can be supported with consistent, well-structured data.
  • Build ETL/ELT data pipelines that move, transform, and prepare enterprise data from source systems into analytical platforms in a repeatable and maintainable way.
  • Develop scalable data architectures for analytics and AI using concepts such as data warehouses and modern data ecosystems.
  • Implement SQL and NoSQL data solutions to manage structured and semi-structured for reporting, analytics, data science, and AI-enabled applications.
  • Use generative AI and AI-assisted tools to support data engineering work, including schema design, pipeline coding, transformation logic, and synthetic data generation.
  • Evaluate data engineering design choices based on business needs such as reliability, scalability and speed of analytics delivery.


Who Should Attend

This course is designed for data analysts, data engineers, AI practitioners, and IT professionals involved in data and analytics initiatives. It is also suitable for managers and business professionals who want to understand modern data pipelines, data ecosystems, and NoSQL technologies for AI and data-driven decision making.


Pre-requisites

  • The prerequisite is either completion of the Data Management for Analytics course or prior experience in the fundamentals of data management/data engineering.
  • Participants should also be comfortable using AI-assisted tools to support coding and analysis.

What to Bring

No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.

If you are bringing a laptop, please see below for the tech specs:

 

Minimum

Recommended

Operating Systems

• Windows 7, 8, 10 or
• Mac OS

Laptop running the latest
version of either Windows or
Mac OS

System Type

32-bit

64-bit

Memory

8 GB RAM

16+ GB RAM

Hard Drive

256 GB disk size

 

Others

• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out)

DirectX 10 graphics card for graphics hardware acceleration

 


What Will Be Covered

  • Introduction of the fundamentals of dimensional modelling and data pipelines for Data Science and AI - Learners will explore OLAP concepts, data warehouse design, schema modelling techniques, and modern data pipeline approaches such as ETL, ELT, and schema management through practical workshops and real-world industry case studies.
  • Implementing data pipelines for both data flow and orchestration - Learners will learn how to manage data movement, transformation, coordination, and governance processes using hands-on activities, group discussions, and case-based learning approaches.
  • Modernised data ecosystems and NoSQL databases for AI-ready applications - Learners will explore concepts such as Data Mesh, Data Fabric, synthetic data generation, and NoSQL database implementation through practical exercises and industry-based case studies.


Fees & Subsidies

SkillsFuture Singapore (SSG) Funding 2026 (Effective 1 July)

Fee ComponentFull Course FeesSingapore Citizens & PRs Aged 21 Years and Above (70% Funding Support)Singapore Citizens Aged 40 Years and Above (90% Funding Support)Enhanced Training Support for SMEs (ETSS) (90% Funding Support)
Full Course FeeS$2,850.00S$2,850.00S$2,850.00S$2,850.00
SSG Funding-S$1,995.00S$1,995.00S$1,995.00
Nett Course FeeS$2,850.00S$855.00S$855.00S$855.00
9% GST on Nett Course FeeS$256.50S$76.95S$76.95S$76.95
Additional Funding if Eligible Under Various Schemes--S$570.00S$570.00
Total Nett Course Fee Payable, Including GSTS$3,106.50S$931.95S$361.95S$361.95

 

Note:

  1. SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
  2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
  3. SME fees are applicable only to participants who are sponsored by small and medium enterprises.
  4. SSG funding is subjected to availability.


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Certificate

The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course and pass the required assessments.



Preparing for Your Course

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

Find out more.

WIFI Access

WIFI access will be made available to participants.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

Click HERE for directions to NUS-ISS

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.



Course Resources

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