MSc Health Data Science

For entry in the academic year beginning September 2026, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £15,800
  International, including EU, students (per annum): £35,700
• PGDip (full-time)
  UK students (per annum): £12,600
  International, including EU, students (per annum): £28,600
• PGDip (part-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (full-time)
  UK students (per annum): £6,300
  International, including EU, students (per annum): £14,300
• PGCert (part-time)
  UK students (per annum): £3,150
  International, including EU, students (per annum): £7,150

Further information for EU students can be found on our dedicated EU page.

The course fees include all the tuition, technical support and examinations required for the course. All fees for entry will be subject to yearly review. Courses lasting more than one year may be subject to incremental rises per annum. For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

The University permits applicants with comparable previous experience to submit an application for consideration of AP(E)L Accreditation Prior (Experiential) Learning. The maximum AP(E)L is 15 credits to a PGCert, 45 credits to a PGDip and 60 credits to a MSc.

If your AP(E)L application is successful, the University charges £30 for every 15 credits of AP(E)L. The overall tuition fee is adjusted and then the administrative charge is applied.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

For the latest scholarship and bursary information please visit the fees and funding page .

The Catherine Chisholm scholarship is applicable to students from selected countries for this course. Find out more details on the scholarship page .

The University of Manchester is proud to offer six fully-funded scholarships to Women from Brunei, Cambodia, Indonesia, Lao PDR, Myanmar, the Philippines, Singapore, Thailand or Timor-Leste completing specific master's courses in STEM subjects. Please visit the STEM scholarship page for more information.

The analysis of multi-modal data has significantly advanced our understanding of complex biological systems and their applications in healthcare. By integrating omics, imaging, and health data using cutting-edge machine learning methods, researchers can infer relationships across diverge modalities. This unit equips students with fundamental knowledge and hands-on computational skills to analyse and interpret such multi-modal datasets effectively.

Three main areas will be covered to give students the range of skills to be able to work with large scale, multi-modal data:

1) Introduction to methods for omics, health data and imaging data analysis

• What is omics data? Introduction to key terminologies. (e.g. multiomics/multiome, single-cell, spatial transcriptomics, spatial proteomics, imaging etc) and health data recap
• Why we need this kind of data?
• From bulk to single-cell – example pipeline of computational analysis e.g. scanpy
• Examples of research studies/published literature

2) Introduction to multi-modal data,

• advantages and disadvantages of multi-modal data analyses/research
• cases studies of multi-modal health research projects

3) Computational methods for analysing multi-modal omics data

• Data integration techniques
• Spatial omics data practical exercise
• Statistical modelling and inference
• Tools and pipelines
• Applications and case studies e.g. LLMs/genAI

The unit aims to:

Introduce the concept of multi-modal data for health and computational techniques applicable to investigate diverse data types. Different case studies will be presented allowing the learners to explore the breadth of this topic and emerging research areas. It is also designed to prepare students for multidisciplinary research by integrating concepts and techniques from multiple domains. 

This module will be delivered in a ‘blended’ learning format over ~6 weeks. Teaching material, including lectures that will be available online either prior to face-to-face (F2F) teaching sessions/tutorials or after live/F2F delivery. Group activities (formative assessments with feedback from tutors and peers) will be facilitated during F2F sessions to consider and reflect on a range of topics/case studies covered in the module. 

Students will be able to:

1. Describe the principles of multi-modal omics, its applications in integrating diverse datasets, and its role in emerging research areas.
2. Demonstrate knowledge of computational techniques (ML/AI) and tools, for analysing multi-modal omics data.
3. Explain the challenges and limitations of integrating multi-modal omics data.
4. Explain the data types and data sources involved in examples of multi-modal data health research 

1. Critically analyse multi-modal omics/health data and compare different computational methods
2. Evaluate scientific and clinical research literature to understand the current research landscape
3. Interpret findings from diverse data technologies 

1. Apply machine-learning approaches for multi-modal data analyses
2. Conduct data analyses on example datasets
3. Develop workflows for multi-modal data analysis, employing methods such as data integration, modelling, and visualisation. 

1. Collaborate in teams that bring together expertise from computational, experimental, and clinical domains.
2. Demonstrate independent problem-solving skills and adaptability in handling diverse and complex datasets.
3. Gain knowledge and hands on experience on ML approaches for integrating multi-modal data which is transferable to other domains.
4. Enhance verbal and written communication skills when appraising and assessing research literature in groups. 

Written feedback provided within 15 days of assignment submission deadline

• Spatial omics technologies at multimodal and single cell/subcellular level: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02824-6
• Single-cell multimodal omics: the power of many: https://www.nature.com/articles/s41592-019-0691-5
• Reccomended Methods and applications for single-cell and spatial multi-omics: https://www.nature.com/articles/s41576-023-00580-2
• The dawn of spatial omics:https://pmc.ncbi.nlm.nih.gov/articles/PMC7614974/
• Single-cell multimodal omics: the power of many https://www.nature.com/articles/s41592-019-0691-5
• MUON: multimodal omics analysis framework https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02577-8
• An approach for integrating multimodal omics data into sparse and interpretable modelshttps://pmc.ncbi.nlm.nih.gov/articles/PMC10921032/