Key Takeaways
At the end of the course, you are expected to be able to:
- Design data models and event schemas optimised for multi-sided platform analytics.
- Build and evaluate retrieval, ranking, and matching pipelines for intelligent discovery systems.
- Diagnose and address recommendation system challenges including cold-start, bias, exploration-exploitation trade-offs, and feedback loops
Who Should Attend
This course is suitable for:
- Early-to-mid career Data Scientists and ML Engineers building discovery and recommendation systems that drive value creation for platform participants.
- Data Engineers and Analytics Engineers who need to ensure their data foundations support the evaluation and optimisation of multi-sided value creation for platform business.
Pre-requisites
- Proficiency in Python programming.
- Foundational knowledge of machine learning (supervised and unsupervised learning, model training and evaluation).
- Basic understanding of SQL and relational databases.
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
- Foundational data structures necessary for multi-sided platform analytics
- Participants will design entity-relationship models for users, items, transactions, and interactions across sides. The curriculum covers event schema design, defining event taxonomies, and building feature engineering and offline computation pipelines.
- Examine data-driven retrieval and ranking pipelines, focusing on candidate generation, scoring, and re-ranking.
- Participants will also implement embedding-based matching and evaluate intelligent discovery systems using offline and online metrics.
- Delve into multi-objective optimisation and the practical trade-offs required in platform recommendation systems.
- Participants will rigorously analyse strategies to balance relevance, diversity, freshness, and competing business KPIs. When addressing the exploration-exploitation trade-off, you will evaluate discovery mechanisms, structuring them as a safe extension of existing user interests rather than relying on true random exploration.
- Tackle advanced system challenges such as cold-start problems, applying hybrid collaborative-content approaches for new user and item onboarding.
- The course analyses feedback loops and metrics traps, teaching participants to navigate popularity bias, filter bubbles and the tension between long-term engagement and short-term clicks.
Fees & Subsidies
SkillsFuture Singapore (SSG) Funding 2026 (Effective 1 July)
| Fee Component | Full Course Fees | Singapore 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 Fee | S$3,800.00 | S$3,800.00 | S$3,800.00 | S$3,800.00 |
| SSG Funding | - | S$2,660.00 | S$2,660.00 | S$2,660.00 |
| Nett Course Fee | S$3,800.00 | S$1,140.00 | S$1,140.00 | S$1,140.00 |
| 9% GST on Nett Course Fee | S$342.00 | S$102.60 | S$102.60 | S$102.60 |
| Additional Funding if Eligible Under Various Schemes | - | - | S$760.00 | S$760.00 |
| Total Nett Course Fee Payable, Including GST | S$4,142.00 | S$1,242.60 | S$482.60 | S$482.60 |
|---|
Note:
- SSG Funding is available to qualified individuals, subject to meeting the attendance requirement and passing of assessment.
- 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.
- SME fees are applicable only to participants who are sponsored by small and medium enterprises.
- SSG funding is subjected to availability.
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.