NUS
 
ISS
 

Platform Analytics for Innovation

Establish the analytical frameworks to test ideas, personalise new offerings and design incentive structures that ignite new supply-demand loops across multi-sided platforms.

Overview

Part of -
Duration 3 days
Course Time
Enquiry Please contact ask-iss@nus.edu.sg for more details.
Multi-sided platforms grow by finding new ways to connect people, discovering new types of participants, new kinds of transactions, and new markets. These expansion opportunities are identified and validated through analytics.

However, on platforms, innovation requires more than just good ideas. You must test ideas rigorously in environments where network effects mean that changing the experience for one group inherently affects everyone else. You need to personalise offerings for participant segments that do not yet exist at scale and design incentive structures capable of bootstrapping new supply-demand loops from scratch.
While most data science programmes teach experimentation and personalisation purely for conversion optimisation on existing products, this course reframes these techniques as tools for platform innovation. Participants will learn how to use causal inference, uplift modelling, and dynamic incentive design not just to optimise existing platform interactions, but to create entirely new ones.

Key Takeaways

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

  • Differentiate platform business types and evaluate corresponding innovation strategies through an experimentation mindset.
  • Design and analyse interference-aware experiments and quasi-experimental studies for platform settings to validate new platform offerings.
  • Build data-driven personalisation systems using identity resolution, context modelling, and uplift optimisation to discover and activate underserved participant segments.
  • Design and optimise dynamic incentive programmes that stimulate new supply-demand loops.



Who Should Attend

This course is suitable for:

  • Early-to-mid career Data Scientists and ML Engineers building experimentation, personalisation, and incentive systems for platform growth.
  • Product analysts, growth analysts, and marketplace strategy analysts responsible for identifying new segments, designing incentive programmes and validating expansion opportunities.

Pre-requisites

  • Proficiency in Python programming.
  • Working knowledge of probability and statistics (hypothesis testing, confidence intervals, regression).
  • Familiarity with machine learning model training and evaluation.

 

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

  • Establish the frameworks for platform-specific innovation and causal inference.
    • Participants will evaluate platform types and develop an experimentation mindset to validate and scale new offerings. The curriculum addresses the potential outcomes framework and network interference, moving into interference-aware experimental designs such as cluster-randomised and switchback experiments. You will also examine quasi-experimental methods, including difference-in-differences and synthetic controls.
  • Focus on discovering and activating underserved participant segments through data-driven personalisation.
    • Participants will implement identity mapping, cross-device identity resolution, and behavioural signal extraction. You will also rigorously analyse user context modelling and uplift optimisation, specifically focusing on conditional average treatment effect (CATE) estimation and treatment effect heterogeneity.
  • Address data-driven incentive strategies designed to bootstrap new market dynamics.
    • Participants will design dynamic incentive programmes, examining cross-side subsidy optimisation and demand-responsive pricing. The course concludes with an integrated case study where you will combine a personalisation strategy, an incentive programme and an experimental design to measure the causal impact of a new supply-demand loop.



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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