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.

Biomedical and health informatics is founded on the usage and application of computational algorithms to high-throughput biological data and patient-level information. With the growing availability of big data in the biomedical domain, machine learning and other advanced approaches are becoming essential. Informaticians need to have a good understanding of these different algorithms, methodologies and analysis pipelines, and their applicability to different data types and research questions. 

This unit aims to:

• Introduce different types of biomedical and health data, including from high-throughput biological experiments and patient-level information resources
• Examine different approaches and pipelines for data analysis
• Learn the principles underlying popular machine learning algorithms
• Gain experience in applying machine learning to real-world datasets
• Critically appraise analysis pipelines and data usage and be able to make suggestions on improvements

• Introduce different types of biomedical and health data, including from high-throughput biological experiments and patient-level information resources
• Examine different approaches and pipelines for data analysis
• Learn the principles underlying popular machine learning algorithms
• Gain experience in applying machine learning to real-world datasets
• Critically appraise analysis pipelines and data usage and be able to make suggestions on improvements

This unit will be delivered in a face-to-face format over six days: lectures and open discussions will provide basic and core knowledge and introduce concrete examples and encourage attendees to draw upon their own reading and experience. Practical sessions will provide hands-on experience, with real-world application of the theoretical material. Coding will be carried out using python via web-based jupyter notebooks that will be available to work on throughout the course. All materials will be introduced on Canva.

• Demonstrate a critical understanding of advanced machine learning methods and techniques
• Apply these methods to a range of problems
• Evaluate different scenarios in which such methods would be applicable
• Assess the possible ethical implications of Machine Learning methods when applied to biomedical data sets

• Critically evaluate the strengths and limitations of different data analysis methods and approaches
• Apply analytical methods to a range of datasets and research questions
• Demonstrate an understanding and draw conclusions from the results of analytical methods

• Produce and execute a data analysis pipeline using python
• Carry out exploratory data analysis using visualisation and clustering methods
• Apply and evaluate the performance of different machine learning algorithms

• Improved coding skills and experience with Jupyter notebooks
• Be able to apply data analysis skills to a broad range of problems
• Use appropriate software and tools for data analysis in biomedical research

3 x interactive online assessments

Three online assessments carried out through interactive notebooks

The three assessments will cover:

1. Exploratory data analysis

2. Supervised machine learning

3. Advanced topics (neural networks and ethics)

.

• Elements of Statistical Learning (Hastie, Tibshirani & Friedman, Springer, 2009, second edition)
• Machine Learning in Healthcare Informatics (Dua et al., Springer, 2014)
• Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Panesar, Apress, 2019)