DSA4262 Sense-making Case Analysis: Health and Medicine (2026)
Undergraduate Year 4 Course, National University of Singapore, Department of Statistics and Data Science, 2026
I am teaching DSA4262 Sense-making Case Analysis: Health & Medicine in 2026 (AY2025/26 Semester 2).
Course synopsis
This course will give you the opportunity to apply your data science skills to real-world problems in healthcare. Through a series of technical workshops and (guest) lectures, we will cover the foundational knowledge required for this course. You will learn about the Singaporean healthcare system, healthcare datasets and their unique characteristics, relevant machine learning approaches including natural language processing, psychology and mental health, and product development frameworks. You will learn how to make sense of real-world problems, facing ambiguous situations and formulating succinct hypotheses.
The course will be interactive and project-based, without a final exam. We will cover healthcare in general, but your group project assignment will be focused on the timely topic of mental health. You will use your skills to (1) build a predictive model and (2) conceptualise and design a real-world solution on top of it in the style of a hackathon.
At the end of the course, you will have a better understanding of healthcare and careers in this industry, more hands-on experience with real-world problem statements, and a GitHub project to add to your portfolio. You will also get opportunities to network with healthcare leaders in Singapore through the guest lectures.
Course content
Data visualisation workshop
You are free to choose your own datasets for the data visualisation assignment. This is a curated list of some datasets, just for your reference or inspiration:
Hackathon lab
We will use the following dataset for the predictive modelling hackathon lab:
Interested students may also join the CLPsych Shared Task challenge (deadline is 18 April 2026), one of the SemEval tasks, or Clinical Natural Language Processing Workshop task:
Clinical Natural Language Processing Workshop Task
Essential readings
These are some strongly recommended readings to build your foundations for this course.
- 📄 A Paradigm Shift in Progress: Generative AI’s Evolving Role in Mental Health Care.
- 📖 Healthcare Transformation Using Artificial Intelligence. Robert JT Morris (2025).
- 📄 Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. Elizabeth Stade et al. (2024).
- 🌐 Mental Health (Our World in Data).
- 🌐 Saloni’s Guide to Data Visualization. Saloni Dattani (2025).
- 📄 Singapore’s health-care system: key features, challenges, and shifts.
- 📄 Tracking the mental health of a nation: Prevalence and correlates of mental disorders in the second Singapore mental health study. Subramaniam et al. (2020).
Deep dives
This course covers a wide range of topics, which would be impossible to cover in a semester. Here are some further materials if you would like to dive a bit deeper on some of the topics, whether it’s technical skills, domain insights, or philosophical intuition.
Foundations (mental healthcare and Singapore)
For building domain empathy and local situational awareness.
- 🎙️ All in the Mind
- 📖 Being Mortal: Medicine and What Matters in the End. Atul Gawande. Discusses the “human” side of clinical outcomes.
- 📖 Essential Psychology: A Concise Introduction.
- 🎙️ Hard Drugs
- 🎙️ Health Check by The Straits Times
- 📖 Hello World: Being Human in the Age of Algorithms. Hannah Fry (2018).
- 📺 Human Behavioral Biology - Stanford Lecture Series. Robert Sapolsky (2012).
- 📖 Losing Our Minds. Lucy Foulkes (2024).
- 📖 Myth or Magic: The Singapore Healthcare System. Jeremy Lim (2013).
- 📖 The Gift of Therapy. Yalom Irvin.
- 📧 The Hemingway Report Newsletter
- 📖 The Laws of Medicine. Siddhartha Mukherjee (2015).
- 📖 The Man Who Mistook his Wife for a Hat. Oliver Sacks.
- 📖 Why Zebras Don’t Get Ulcers. Robert Sapolsky.
Toolkit (data science and visual communication)
For mastering the engineering of perception and technical methodology.
- 📖 Calling Bullshit: The Art of Skepticism in a Data-Driven World. Carl Bergstrom and Jevin West.
- 📄 Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.
- 📖 Speech and Language Processing. Dan Jurafsky and James H. Martin (2026).
- 📄 The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Yla R. Tausczik and James W. Pennebaker (2009).
- 📄 The Science of Visual Data Communication: What Works.
Building the future (GenAI and product building)
For understanding the cutting edge and its implementation challenges.
- 📖 AIQ. Nick Polson and James Scott (2018).
- 📖 Co-Intelligence. Ethan Mollick (2024).
- 📊 Model AI governance framework for Generative AI
- 🎙️ One North Stories
- 📊 State of Clinical AI Report 2026
- 📖 The AI Revolution in Medicine: GPT-4 and Beyond. Peter Lee, Carey Goldberg, and Isaac Kohane (2023).
- 📖 The Design of Everyday Things. Donald Norman (1988).
- 📖 The Innovator’s Prescription. Clayton M. Christensen, Jerome H. Grossman, and Jason Hwang (2008).
- 📖 Working Backwards. Colin Bryar.
The critical lens (validity, ethics, philosophy, mental models)
For avoiding the “Cargo Cult” trap and ensuring clinical utility.