Overview
| Part of |
-
|
| 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.