Graduate Certificate in Intelligent Platform Analytics

The Graduate Certificate in Intelligent Platform Analytics is a graduate-level analytics certificate offered by the NUS-ISS at the National University of Singapore. Designed for data professionals, product managers, and business analysts seeking to advance toward a Master's degree, it equips learners with the end-to-end capabilities required to connect strategic business logic with analytical execution across multi-sided platforms.

Covering the full analytics lifecycle of multi-sided platforms, spanning business strategy, data-driven value creation, system operations and experimental innovation, the programme provides both the strategic foundations and technical skills needed to launch, scale, govern, and grow these complex environments. Participants will complete four modular courses over 14 training days, complemented by a hands-on Practice Project that reinforces learning through real-world application.

The Graduate Certificate in Intelligent Platform Analytics is stackable towards the Master of Technology in Enterprise Business Analytics (MTech EBAC), providing a flexible pathway for professionals pursuing advanced qualifications in platform strategy and data science.

Key Learning Outcomes 

Upon completion of the Programme, participants will be able to:

• Analyse platform business models, network effects, and competitive dynamics to inform analytics strategy and prioritisation.
• Design multi-sided platform data models, event schemas and feature engineering pipelines for analytical consumption.
• Build and evaluate intelligent discovery, recommendation, and matching systems using retrieval-ranking pipelines and embedding-based methods.
• Design platform health metrics, operational telemetry, and forecasting systems for multi-sided environments.
• Deploy real-time ML inference systems with appropriate serving, monitoring and drift detection strategies.
• Implement governance and trust-and-safety frameworks including fairness auditing, anomaly detection, and synthetic data generation.
• Design and execute interference-aware experiments and quasi-experimental analyses for platform environments with network effects.
• Develop data-driven personalisation, dynamic pricing, and incentive strategies using uplift modelling and treatment effect estimation.
• Communicate analytics design decisions, evaluation results and strategic recommendations to technical and business stakeholders.