Specialist Diploma in Business Analytics with Agentic AI
Open for Registration
Open for Registration
Course Type
Specialist Diplomas
Infocomm
Artificial Intelligence
Data Analytics & Visualisation
The Specialist Diploma in Business Analytics with Agentic AI empowers students to transform data into intelligent action.
This course is open for registration. Apply here. (opens in new tab)
About course
Course overview
The Specialist Diploma in Business Analytics with Agentic AI empowers students to transform data into intelligent action. Building a strong foundation in analytics and generative AI before advancing to agentic AI, the programme prepares diploma and degree holders and working professionals from all backgrounds to automate workflows, unlock insights, and lead data-driven decisions. Graduates emerge ready to harness AI as a competitive advantage in a rapidly evolving business world.
Through practical, industry-focused learning, you will:
Develop practical skills in business analytics, generative AI and agentic AI
Apply AI and analytics to real-world business challenges
Build and automate intelligent workflows to improve business processes
Transform data into actionable insights for informed decision-making
Gain hands-on experience with industry-relevant tools and applications
Develop the confidence to harness AI as a competitive advantage
Course objectives
Upon completion of the programme, graduates of SDBAAI will be able to:
Prepare data using SQL, data warehousing, and ETL workflows, and analyse data to design visualisations and dashboards that support business decisions.
Apply machine learning techniques within the CRISP-DM framework and conduct text analytics combining classical methods with LLMs for sentiment evaluation, topic detection, and entity recognition.
Control and verify AI model outputs by crafting systematic prompts, tuning model behaviour, and applying structured verification techniques to ensure reliable and reproducible results.
Design, deploy, and govern AI agents (including multi-agent systems) that automate end-to-end analytics workflows, and evaluate and debug agent behaviour through traces and error analysis.
Navigate ethical, governance, and regulatory considerations for agentic and generative AI deployment in analytics practice, including hallucination risk, bias, data leakage, and adversarial threats.
Target audiences
Diploma or degree level graduates and professionals who want to strengthen their data analytics capabilities while learning to integrate generative AI and agentic AI in their analytics workflow.
Course structure
You will be awarded the Post-Diploma Certificate (PDC) upon successful completion of each certificate programme. The Specialist Diploma in Business Analytics with Agentic AI will be issued upon meeting 50% of attendance requirement and passing the assessment criteria of both PDCs.
Post-Diploma Certificate in Generative AI and Agentic Data Analytics
Post-Diploma Certificate in Generative AI and Agentic Data Analytics
Module name | Module synopsis |
|---|---|
Prompt Engineering Fundamentals | Learn how LLMs generate responses and their limitations including bias and hallucination. Covers prompting techniques (chain-of-thought, few-shot, role-based), model behavior customization through system prompts and parameter tuning, prompt chaining for multi-step reasoning, and verification of AI-generated outputs. Includes context engineering such as source document selection, retrieval structuring, and conversation memory management. |
Generative AI Tools and Workflows | Hands-on practice with code-free AI platforms across analytics-oriented business tasks. Covers report generation, data summarization, AI-generated visuals, data analysis and visualization, process automation, and conversational data interfaces. |
Ethical and Societal Impact of Generative AI | Explore ethical considerations and regulatory frameworks for GenAI, including Model AI Governance Framework for Generative AI (IMDA / AI Verify Foundation). Covers hallucination, bias in decision-making, data leakage, adversarial risks, stakeholder engagement, and responsible application through case studies. |
AI-Enhanced Data Preparation | Covers data preparation for analytics through ETL processes, working with relational databases, data warehouse architecture, and SQL querying. GenAI assists with generating SQL from natural language, detecting data quality issues, and metadata extraction. Includes a visual data pipeline platform that incorporates GenAI within the workflow, plus data governance, ethics, and security. |
Agentic AI in Data Analytics | Design and deploy AI agents that automate analytics tasks using four industry design patterns: reflection, tool use, planning, and multi-agent collaboration. Progresses from single agents to multi-agent systems using code-based frameworks and visual automation platforms. |
Post-Diploma Certificate in Generative AI for Visual, Predictive, and Text Analytics
Table caption
Module name | Module synopsis |
|---|---|
AI-Augmented Data Visualization and Storytelling | Analyse and visualise data for business decisions with GenAI embedded throughout. Covers visualisation principles, data storytelling, and dashboard design. GenAI capabilities include natural language querying, smart chart recommendations, insight discovery, and narrative generation. Students evaluate and tailor AI-generated visualisations for different audiences. |
Statistics and Predictive Analytics | Builds statistical foundations (descriptive analytics, probability, hypothesis testing, regression) in applied business contexts, progressing to predictive and prescriptive analytics within CRISP-DM. Students manually build and evaluate models before advancing to AutoML. GenAI supports assumption verification, code generation for predictive analytics, and translating model findings into business insights and recommendations. |
AI for Text Analytics and Insights | Teaches a structured text analytics pipeline. Classical techniques (preprocessing, TF-IDF, classification models) for scalable sentiment scoring, topic detection, and entity recognition, then LLMs selectively for summarization, semantic search, and contextual interpretation. Students construct effective context for LLM tasks through document selection, analytical memory, and grounded input structuring. Covers technique selection based on scale, cost, and accuracy, with verification through ground truth sampling. |
Entry requirements
Applicants should possess a relevant (Business, Engineering, or Technology) local polytechnic Diploma, ITE Technical Diploma / Technical Engineer Diploma / Work-Study Technical Diploma or Degree.
Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least five years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted will have to go through an interview and/or entrance test. The polytechnic reserves the right to shortlist and admit applicants.
Conditional Offer
Graduating students (in final semester) from a local polytechnic or Institute of Technical Education (ITE) may apply for the programme.
Click here (opens in new tab) for more information on how to apply for the programme and receive a Conditional Offer.
How to apply?
Click the Apply button to submit your application. You will receive a confirmation once it has been successfully submitted.
If you are unable to attend the current course dates, click Register Interest button to be notified of future intakes.
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Company Sponsorship
If your company is sponsoring your course:
Ask your company’s course coordinator to log in to the STEP portal with Corp Pass and create a corporate application link for you.
Use this link to submit your application.
Need help? Check out our step-by-step guide ↗(opens in new tab) (opens in new tab) for instructions on using the STEP portal.
Need more information?
Visit the Course Application page (opens in new tab) for full details on the application process.