Teaching & modules
Modules
Required modules
You are required to take:
MSc
- Statistical Modelling for Health Data (30 credits)
- Health Informatics and Predictive Analytics (30 credits)
- ASMHI Research Project (60 credits)
- ASMHI Skills & Futures Series
PGDip
- Statistical Modelling for Health Data (30 credits)
- Health Informatics and Predictive Analytics (30 credits)
- ASMHI Skills & Futures Series
PGCert
- Statistical Modelling for Health Data (30 credits)
or
- Health Informatics and Predictive Analytics (30 credits)
Optional modules
Students will choose four optional modules from the list below, each worth 15 credits:
- Multilevel and Longitudinal Modelling (15 credits)
- Causal Modelling and Evaluation (15 credits)
- Machine Learning for Health and Bioinformatics (15 credits)
- Clinical Trials: A practical approach (15 credits)
- Natural Language Processing (15 credits)
- Latent Variable and Structural Equation Modelling (15 credits)
- Artificial Intelligence for Healthcare Analytics (15 credits)
- Bioinformatics, Interpretation and Data Quality in Genome Analysis (15 credits)
Seminars and workshops
To support your development and enhance your learning experience, our programme offers a range of structured and interactive opportunities.
The ASMHI Skills & Futures Series runs throughout the year and is a core component of the programme. It includes a mix of workshops and seminars designed to support your growth across academic, research, personal, and career domains. Sessions are delivered through two strands: the Core Skills Pathway, focused on essential competencies, and Friday Futures, which explores innovation and emerging trends in health informatics and statistical modelling.
You are also encouraged to attend optional departmental and research seminars each term. These offer valuable opportunities to engage with current developments in the field, hear from academic and industry experts, and broaden your understanding of the health data science landscape.
To help you prepare for the programme, we offer two optional pre-course bootcamps in Programming and Statistics—ideal for those new to these areas or looking to refresh key concepts before the start of term.
These sessions are designed to enrich your learning, build your confidence, and ensure you’re well prepared to succeed on the programme and beyond.
Teaching methods - what to expect
Our course combines core training in statistical modelling and predictive analytics with advanced modules in areas such as machine learning, artificial intelligence, causal inference, natural language processing, latent variable modelling, and clinical trials.
We use a variety of teaching methods—blended learning, in-person lectures, workshops, seminars, and digital activities—to ensure an engaging and flexible experience.
Skills & Futures Series
As part of your development, you will take part in the Skills & Futures Series – a structured set of workshops supporting growth across four core domains: academic practice, research capability, personal effectiveness, and career readiness. The series includes two strands:
- The Core Skills Pathway, which focuses on essential competencies for success, and
- Friday Futures, a programme of fortnightly workshops exploring cutting-edge developments in health informatics, data science, and statistical modelling.
Friday Futures sessions combine lectures, hands-on practicals, and guest talks from experts in academia, industry, and the NHS. Topics include causal machine learning, target trial emulation, and mobile health (mHealth) data analysis, alongside industry-relevant skills such as cloud computing (e.g. AWS), high-performance computing (HPC), version control, and containers. You’ll also gain insights into entrepreneurship, the pharma and health tech sectors, and career opportunities beyond academia.
Together, these strands help you build a strong foundation and prepare for a wide range of futures in research, healthcare, and data science.
You will also complete a programme-level ePortfolio – a capstone assessment that documents your progress and showcases your technical abilities, reflective practice, and professional development. It enables you to build a personal brand and articulate your achievements to employers, while also developing high-demand skills aligned with priorities identified by the World Economic Forum.
During the one-year programme, you will complete:
MSc
- Two core modules: Statistical Modelling for Health Data (30 credits) and Health Informatics and Predictive Analytics (30 credits)
- Four optional modules of your choice (15 credits each)
- A research project (60 credits)
- The ASMHI Skills & Futures Seminar Series
These components total 180 credits for the MSc. Optional modules are listed below.
PGDip
- Two core modules: Statistical Modelling for Health Data (30 credits) and Health Informatics and Predictive Analytics (30 credits)
- Four optional modules of your choice (15 credits each)
- The ASMHI Skills & Futures Seminar Series
These components total 120 credits for the PGDip. Optional modules are listed below.
PGCert
- One core module: Statistical Modelling for Health Data (30 credits) or Health Informatics and Predictive Analytics (30 credits)
- Two optional modules of your choice (15 credits each)
- The ASMHI Skills & Futures Seminar Series (Optional)
These components total 60 credits for the PGCert. Optional modules are listed below.
| Module name | Lectures (hours) |
Seminars/tutorials (hours) |
Self-directed study (hours) |
Total (hours) |
|---|---|---|---|---|
| Statistical Modelling for Health Data | 40 | 60 | 200 | 300 |
| Health Informatics and Predictive Analytics | 20 | 30 | 100 | 150 |
| Multilevel and Longitudinal Modelling | 20 | 30 | 100 | 150 |
| Causal Modelling and Evaluation | 20 | 30 | 100 | 150 |
| Machine Learning for Health and Bioinformatics | 20 | 30 | 100 | 150 |
| Clinical trials: A practical approach | 20 | 30 | 100 | 150 |
| Natural Language Processing | 20 | 30 | 100 | 150 |
| Latent Variables and Structural Equation Modelling | 20 | 30 | 100 | 150 |
| Artificial Intelligence for Healthcare Analytics | 20 | 30 | 100 | 150 |
| Bioinformatics, Interpretation and Data Quality in Genome Analysis | 20 | 30 | 100 | 150 |
| ASMHI Research Project | - | 60 | 540 | 600 |
| ASMHI Skills & Futures Series | 10 | 70 | 20 | 100 |
Typically, one credit equates to 10 hours of work.
Location
Our course is primarily taught on campus at the King’s College London Denmark Hill Campus.
Assessment
- Coursework
- Coding assignments
- Presentations
- Group projects
- Posters
- Role-play exercises
Our programme is designed to equip you for success—whether you’re pursuing a career in health data science, further academic study, or roles in healthcare innovation and policy. You’ll be assessed through a diverse and engaging mix of formats that reflect real-world practice and support your professional development. Assessments include written coursework, coding assignments, presentations, group projects, poster sessions, and even role-play exercises that simulate applied challenges in health data and policy. This variety helps you develop a broad skillset—combining technical proficiency with communication, collaboration, and problem-solving. You’ll also complete a programme-level ePortfolio that documents your technical, academic, and professional growth. Designed to help you articulate your personal brand and showcase your achievements, the ePortfolio builds in-demand competencies such as creativity, adaptability, communication, and lifelong learning—key attributes identified by organisations like the World Economic Forum. A highlight of the programme is the Research Project module. Working with real-world data from ongoing research, you’ll address applied questions and often collaborate with clinicians, researchers, or external stakeholders. This provides valuable experience in cross-sector settings and the opportunity to contribute to meaningful work. Your project will culminate in a written report that prepares you for academic and professional publication. You’ll also present your findings through a poster and viva, simulating a professional or conference environment and strengthening your ability to communicate complex ideas clearly and confidently. Together, these assessments enhance your employability and foster both professional and personal growth—giving you the practical experience and confidence to thrive in your next steps.
Application closing date guidance
Key Information
Course type:
Master's
Delivery mode:
In person / Classroom & Online
Study mode:
Full time
Duration:
One Year
Application status:
Open
Start date:
September 2026