Note: I will be teaching this course again in AY2025/26 Semester 2 at NUS. Please check back in December 2025 for more information.
Course description
Interested in applying your data science skills for the public good? In this course, we will examine how data science and machine learning can be used to inform government policymaking and enhance service delivery. We start with an overview of Singapore's public sector and the range of policy issues Singapore grapples with, before focusing on specific case studies to understand how data science works in practice. You will also have the chance to sharpen your skills in data visualisation, technical communication, and other data science skills. The course culminates in a group project focused on applying data science to specific public sector use cases, ranging from geospatial data analysis to natural language processing. Join us on an exciting journey of learning how to use data science for the public good!
What you will learn from this course:
- How data science is used in the Singapore public sector
- Practical skills for delivering good data science and AI projects
- Working in a team to apply data science to a public policy problem
View AY2024/25 Projects →
Course outline
In the first half of the course, we go through content and skills that will help you for your group project, covering both technical aspects, like geospatial and text data analysis, and crucial soft skills, like technical communication and project scoping. In the second half of the course, you will be given time to focus on your group projects, with optional consultations to help you along the way. The group presentations will be held in the last two weeks of the semester.
- Week 1: Data science in the Singapore public sector
- Week 2: Analysing geospatial data
- Week 3: Analysing text data
- Week 4: AI safety and fairness
- Week 5: Communication & Visualisation
- Week 6: Scoping data science projects
- Week 7: Group project kickoff
- Week 8-11: Group project (and consultations)
- Week 12: Presentations (Problem 1)
- Week 13: Presentations (Problem 2)
Note that this is an indicative outline and subject to change.
Course requirements
To do well in this course, students should:
- Have a strong understanding of key machine learning and data science concepts
- Be proficient in programming with R or Python
- Be comfortable with essential development tools (e.g. Git, venv, Docker)
- Be confident in communicating technical ideas to different audiences
- Have a basic grasp of the Singapore public sector and policy issues
- Be interested in applying data science to public policy problems
About the lecturer
I am a Senior Data Scientist at GovTech Singapore. I'm currently the technical co-lead for the AI Practice's Responsible AI team, which focuses on applied research and experimentation for AI safety, fairness, and robustness. Our team has developed and open-sourced several tools, such as LionGuard for localised safety (see version 1 and version 2), KnowOrNot for out-of-knowledge-base robustness, LLM guardrails for off-topic prompts and system prompt leakage, and benchmarks like MinorBench (child safety) and RabakBench (localised safety). We also have a Responsible AI playbook to improve the public sector's technical understanding of key responsible AI concepts and tools.
Prior to this, I was the data science team lead at the Ministry of Manpower's Co-Lab unit, working on a wide range of data analytics, machine learning, and LLM-related projects to support the ministry's policymaking and operations. Before joining the Singapore government, I was the Lead Data Scientist at Lovelytics in Washington D.C., where I was responsible for the company’s data science projects with external clients.
I graduated from Columbia University in 2019 with a MA in Quantitative Methods in the Social Sciences (Data Science Focus), and from the University of Oxford in 2018 with a BA (Hons) in Philosophy, Politics and Economics.
See my website here for more details.