AI for Data Professionals
About This Course
This course is designed for data professionals across industries who want to move beyond traditional analytics and harness the power of artificial intelligence (AI) in their day-to-day workflows. Whether you're a data analyst, business intelligence professional, or technical team member, this course equips you with practical, hands-on skills to integrate AI tools into insight generation, pipeline automation, and model deployment.
You’ll learn how to apply prompt engineering techniques, use large language models (LLMs) via APIs, and design scalable, production-ready AI systems using industry-standard tools like LangChain and MLOps frameworks. By the end of the course, you'll be able to confidently evaluate, implement, and manage AI-powered workflows that drive measurable value for your organisation.
If you're ready to go from experimenting with AI to building intelligent systems that work in the real world, this course is for you.
What You'll Learn
- Apply AI techniques to improve existing analytics and reporting workflows in business contexts.
- Use structured evaluation methods to determine the viability of AI opportunities in data projects.
- Demonstrate how AI tools can improve insight generation and streamline exploratory analysis.
- Experiment with prompt strategies to enhance AI model responses for data tasks.
- Use AI-based automation to streamline repetitive data preparation and transformation tasks.
- Demonstrate the use of AI development tools to accelerate coding and data engineering tasks.
- Compare model integration patterns across sectors to inform appropriate AI deployment strategies.
- Interpret AI model architecture and performance to select suitable language models for NLP use cases.
- Implement AI lifecycle strategies to manage model reliability, scalability, and operational risks.
- Apply tuning methods to optimize AI models for specific business functions and efficiency needs.
- Build and assess AI prototypes by applying best practices from across industry applications.
- Design AI applications using multimodal and retrieval-based architectures to address business problems.
Entry Requirements
Academic Requirement
- Secondary or higher
Language Proficiency
- GCE O Level English with a pass (i.e. C6 of above) or
- Workplace Literacy & Numeracy (WPLN) Level 5
Experience
- Minimum 21 years old
- 1 - 2 years of work experience recommended to optimize learning experience
If you do not have the minimum entry requirements, please contact our Admissions Team and we will assess on a case-by-case basis.