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 one of the currently ongoing Shared Task challenges:

Essential readings

These are some strongly recommended readings to build your foundations for this course.

  1. 📄 A paradigm shift in progress: Generative AI’s evolving role in mental health care. John Torous and Andrea Cipriani (2025). - A recent perspective on the future of GenAI in mental health.
  2. 📖 Healthcare Transformation Using Artificial Intelligence. Robert JT Morris (2025). - A great resource on AI’s transformative role in healthcare, with many case studies from Singapore.
  3. 📄 Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation. Elizabeth Stade et al. (2024). - A nice overview and roadmap of responsible LLM deployments in behavioral healthcare.
  4. 🌐 Mental Health (Our World in Data). - Beautiful graphs and rich datasets to increase your understanding of global mental health.
  5. 📄 Rules of ML - Essential reading for ML engineering.
  6. 🌐 Saloni’s Guide to Data Visualization. Saloni Dattani (2025). - Good introduction to the importance of data visualization and some practical advice.
  7. 📄 Singapore’s health-care system: Key features, challenges, and shifts. Chorh Chuan Tan et al. (2021) - Great introduction to Singapore’s healthcare system and national priorities.
  8. 📄 Tracking the mental health of a nation: Prevalence and correlates of mental disorders in the second Singapore mental health study. Subramaniam et al. (2020). - Good local overview of mental health prevalence rates.

Deep dives

This course covers a wide range of topics, which would be impossible to cover in a single 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. These are all quite accessible, suitable for final year undergrad or early postgrad students.

Foundations (mental healthcare and Singapore)

For building domain empathy and local situational awareness.

  1. 🎙️ All in the Mind. - Amazing humanistic podcast on mental health from Australia’s ABC.
  2. 📖 Being Mortal: Medicine and What Matters in the End. Atul Gawande (2014). - Discusses the “human” side of clinical outcomes.
  3. 📖 Essential Psychology: A Concise Introduction. - Philip Banyard, Mark Davies, and Christine Norman (2010). - A good entry-level textbook to start understanding psychology better.
  4. 🎙️ Hard Drugs. - Podcast by Works in Progress.
  5. 🎙️ Health Check by The Straits Times. - Local healthcare podcast by the Straits Times.
  6. 📺 Human Behavioral Biology - Stanford Lecture Series. Robert Sapolsky (2012). - Slightly outdated, but amazing lecture series about human behavioural biology.
  7. 📖 Losing Our Minds. Lucy Foulkes (2024). - A great and grounded recent update on modern trends in mental health.
  8. 📖 Mental health of a nation. Ng Beng Yong and Daniel Fung (2017). - A good overview of mental health in Singapore.
  9. 📖 Myth or Magic: The Singapore Healthcare System. Jeremy Lim (2013). - For those trying to better understand Singapore’s famously strong healthcare system.
  10. 📖 The Gift of Therapy. Yalom Irvin (2001). - Beautifully written, this will help you understand what psychotherapy is about.
  11. 📧 The Hemingway Report Newsletter. - To understand commercial trends in the mental health industry.
  12. 📖 The Laws of Medicine. Siddhartha Mukherjee (2015). - To understand the medical discipline better.
  13. 📖 The Man Who Mistook his Wife for a Hat. Oliver Sacks (1985). - One of the best clinician writers, this will take you through several human studies in psychiatry.

Toolkit (data science and visual communication)

For mastering the engineering of perception and technical methodology.

  1. 🌐 AI Explorables. - Repository of data visualisations and model interpretations.
  2. 📖 Calling Bullshit: The Art of Skepticism in a Data-Driven World. Carl Bergstrom and Jevin West.
  3. 📄 Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. - Introduction to digital phenotyping, a promising methodology in mental health.
  4. 📖 Pattern Recognition and Machine Learning. Chris Bishop (2006). - A classic textbook for learning the foundations of machine learning.
  5. 📖 Speech and Language Processing. Dan Jurafsky and James H. Martin (2026). - One of the most popular textbooks for learning NLP.
  6. 📄 The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Yla R. Tausczik and James W. Pennebaker (2009). - One of the popular frameworks for semantic analysis of natural language before the era of LLMs.
  7. 📄 The Science of Visual Data Communication: What Works. - A rigorous evaluation of what works in visual communication.

Building the future (GenAI and product)

For understanding the cutting edge and its implementation challenges.

  1. 📖 AIQ. Nick Polson and James Scott (2018). - For understanding how to build human-centered AI products.
  2. 📊 ARISE State of Clinical AI Report 2026. - Recent Stanford report on the state of clinical AI.
  3. 📖 Co-Intelligence. Ethan Mollick (2024). - Discussion of our relationship with AI and how to move forward.
  4. 📊 Model AI governance framework for Generative AI. - A local AI governance framework.
  5. 🎙️ One North Stories. - A local podcast that discusses high-tech innovation startups.
  6. 📊 PAIR Guidebook. - Guidebook on how to design AI products by team at Google.
  7. 📖 The AI Revolution in Medicine: GPT-4 and Beyond. Peter Lee, Carey Goldberg, and Isaac Kohane (2023). - On how LLMs will revolutionise healthcare.
  8. 📖 The Design of Everyday Things. Donald Norman (1988).
  9. 📖 The Innovator’s Prescription. Clayton M. Christensen, Jerome H. Grossman, and Jason Hwang (2008).
  10. 📖 Working Backwards. Colin Bryar.

The critical lens (validity, ethics, philosophy, mental models)

For avoiding the “Cargo Cult” trap and ensuring clinical utility.

  1. 📄 Cargo Cult Science. Richard Feynman. - You should read this article at least once every year.
  2. 📄 Construct Validity In Psychology Research. - Discusses “construct validity” and why it’s so important in psychology.
  3. 📖 Hello World: Being Human in the Age of Algorithms. Hannah Fry (2018). - Will make you think more critically about data and algorithms.
  4. 📄 Illusory generalizability of clinical prediction models. - Makes you more critical when reading about academic articles promising high accuracy.
  5. 📄 Perspectives on Machine Learning from Psychology’s Reproducibility Crisis. Bell and Kampman (2021). - A discussion on what the machine learning research community can learn from psychology’s reproducibility crisis.
  6. 🌐 Robust Behavioural Science. Amy Orben (2021). - Amazing course on how we can make psychology a more robust discipline.
  7. 📄 The Theory Crisis in Psychology: How to Move Forward. Markus Eronen and Laura Bringmann (2021). - Interesting perspective on the fundamental difficulty of advancing psychology as a science.

The synthesis (intersection of AI, psychology, and art)

If this course resonates with you on a more personal level, these are some resources beyond the scope of the course. For exploring the fascinating intersection of AI, psychology, and art even further.

  1. 🎬 All Watched over by Machines of Loving Grace. - Old school (2011) BBC documentary about the relationship between humans and machines.
  2. 📄 Conversation self-play for psychotherapy discovery and understanding. Kampman et al. (2025). - A recent paper of mine suggesting that LLMs can help discover new ways of doing psychotherapy.
  3. 🌐 Dimensional App. - A cool mobile app for a huge range of psychometric testing and learning about yourself, and a great demonstration of data science’s role in psychology.
  4. 📖 Godel, Escher, Bach - Many cognitive scientist and computer science students have (tried to) read this famous book, it’s quite mind-bending!
  5. 🎬 Her (Film). - Fun movie from 2013 that makes you reflect on the feasibility and desirability of AI companionship.
  6. 📖 The Age of Insight. - Ambitious book written by a Nobel Prize winning neuroscientist, it explores the history of cognitive from 1900 Vienna onwards and discusses the central role of art.
  7. 📖 What makes us smart. Samuel Gershman (2021). - A great conceptualisation on how to think about cognition through a Bayesian lens, which makes you rethink what irrationality (and mental illness) really are.
  8. 📖 The World After Capital. - Makes the case that attention is the main scarcity now (relevant for social media’s role in mental health).