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.

Expanding on traditional epidemiological approaches this unit will cover:

Key sources of digital data, data types, data collection and integration, and its use in health-related studies, e.g., digital data generated from sources such as social media, search engines, mobile health apps, wearable devices, electronic health records, and online surveys. And how these are integrated into epidemiological research.

Topics will cover, real-time surveillance and monitoring, social medial and digital biomarkers, touch on machine learning and big data analytics – with case study examples.

Mobile Health and telemedicine: the role of mobile health technologies and telemedicine in epidemiological research and public health interventions. This includes studying mobile apps for symptom tracking, virtual consultations, remote monitoring of chronic diseases.

Covering key concepts of digital data protection, legislation, and ethics. Examining ethical and legal issues related to data privacy, consent, confidentiality, and responsible data use.

By integrating digital technologies and computational methods for epidemiological research, students will gain knowledge for improving public health surveillance, research, and intervention strategies in an increasingly digital world.

The unit aims to:

This module aims to build on the principles of epidemiology through the lens of a digital world.

This unit will introduce students to the concept of digital epidemiology and the study population health and disease using remotely and/or automatically collected data 1from a variety of sources. This fits well with other units in the MSc where students learn core skills for health data analysis (e.g. statistics), whilst providing them with a wider understanding of epidemiology in the digital era. The module aims to give students real examples of studies that utilise digital systems to inform policy and deliver effective digital interventions within the healthcare system (e.g., risk prediction models based on statistical, remote patient monitoring, AI approaches and knowledge support systems within electronic health record systems)

This will be delivered though a series of: 

• In-person lectures,
• e-learning reading materials,
• Group-based work assignment,
• Individual assignment.

Students should be able to: 

A1 Identify and describe diverse sources of digital data. 

A2 Explain the advantages and limitations of digital epidemiology compared to traditional epidemiological approaches, including real-time surveillance, scalability, and potential biases inherent in digital datasets. 

A3 Demonstrate a critical understanding of the principles of data collection, processing, and analysis in digital epidemiology, including data cleaning, validation, and statistical methods for analysing large-scale digital datasets. 

A4 Discuss ethical and legal considerations for digital epidemiology, including issues of data privacy, consent, confidentiality, and responsible use of data.

Students should be able to:

B1 Critically evaluate the validity, reliability, and relevance of digital epidemiological research, including evaluation of methodologies, data sources, and any biases 

B2 Develop problem solving skills – to be able to design solutions using digital resources to health research questions. 

B3 Outline steps required to ensure issues of data privacy, consent, confidentiality, and equity are addressed.

Students should be able to: 

C1 Demonstrate the ability to conceptualise and design the integration of diverse digital data types to address a specific health research question or public health problem. 

C2 Select appropriate data collection and tools to optimise data utility and reliability and protection. 

C3 Apply knowledge of analytics to answer health research questions with integrated data types

Students should be able to: 

D1 Collaborate effectively with group-based work. 

D2 Develop independent academic writing skills.

Feedback on the group presentation will be given during the session, peer feedback via voting on metrics provided for best student voted project. 

Formative feedback will be provided to groups by teaching staff in the form of written feedback and final group marks. 

Further feedback on individual assignments will be supplied via Blackboard. 

Digital Epidemiology: An introduction to disease surveillance using digital data. Walker M 2022