The MSt in Healthcare Data Science course is designed to empower learners by integrating scientific principles with a comprehensive range of behavioural, managerial, and technical skills. The goal is to cultivate highly competitive professionals capable of playing effective roles in health data science projects. The design of the course considers the multidisciplinary nature of this field and incorporates required components from mathematics, statistics, computation, and health sciences to reflect the skills required for level 7 as well as incorporation of knowledge and skills outlined by industry.
The programme has been developed by a multidisciplinary team of academics and researchers from University of Cambridge Professional and Continuing Education (PACE), Cambridge University Hospital, and the School of Clinical Medicine. It is taught part-time and is designed to be flexible and accessible to working healthcare professionals.
Course Dates
Course details
Tutors
Key Features
Aims
The course will:
- provide teaching and learning opportunities to gain the knowledge and skills that are at the forefront of the successful implementation of an advanced health-focused data science project
- equip learners with current data science tools and techniques to manage and analyse large datasets across healthcare systems
- advance learners’ programming and analytical skills for performing meaningful and reproducible analysis
- develop, create and upskill healthcare data experts with the necessary expertise, and originality of application, to pursue and expand their roles in the context of the rapidly evolving environment of electronic health data
- promote a comprehensive understanding of the practical and ethical considerations relevant to health data, informatics and innovation
- provide work relevant learning opportunities and practical expertise in the context of a critical awareness of current problems, best-practice, challenges, and potential solutions in the use of health data
- provide students with advanced knowledge and skills required for design and execution of the health data science project capturing: the entire process from initial curiosity driven database queries, through to data analysis, statistical inference and visualization in an impactful and reproducible output
- all Master's students will receive supervision to develop the required elements for leading a project in a work-relevant and practical manner delivered via their research dissertation
The programme provides the advanced skills and knowledge required to work and play an effective role in a rigorous health focused data science project.
Student support
Depending upon your needs, a variety of support opportunities are available to you including wellbeing support sessions, short-term counselling, and study skills support sessions. Find out more in our student support webpages.
Expected academic standard
Applicants for this course are expected to have achieved a UK 2.i honours degree or equivalent.
Language requirement
If English isn’t your first language, you will be required to submit evidence that you meet the University’s English language requirement before you are admitted.
Please see full details on the University Language Requirement webpage.
Language requirements for this course are below:
- IELTS Academic: Overall band score of 7.5 (with a minimum of 7.0 in each individual component)
- TOEFL Internet: Overall score of 110 (a minimum of 25 in each individual component)
- C1 Advanced: Grade A or B (with at least 193 in each individual element), plus a Language Centre assessment.
- C2 Proficiency: Grade A, B, or C (with at least 200, with no element lower than 185)
There are no exceptions to this requirement and, if you are offered a place on the course, it will be subject to you meeting this requirement.
Visa information
Students registered on a part-time Master of Studies (MSt) will be able to attend the short teaching sessions with a visitor status in the UK. Entry to the UK as a visitor has a number of expectations and restrictions which you should consider carefully.
Further information is provided on the International Students website and prospective students are advised to read this in full.
Students attending sessions taking place at intervals across the year with a visitor status are expected not to remain in the UK for extended periods. The majority of study must be undertaken outside the UK and generally students will be required to leave the UK at the end of each session and return for the next. As a visitor on a course of more than 6 months, it is not possible to make the UK your main study location or residence, or make frequent or successive visits to stay in the UK for extended periods.
The MSt Healthcare Data Science is a part-time Master's course designed to fit with the demands of full-time employment. The course is delivered through a combination of face-to-face sessions requiring attendance in Cambridge (blended with remote learning where suitable), plus self-directed learning supported through a virtual learning environment [VLE].
Teaching
Full in-person attendance is required at the teaching blocks which are held on a bi-monthly basis commencing October 2025. The Master class sessions will take place online.
Please note that the MSt is not eligible for University visa sponsorship and therefore international students would need to obtain their own immigration permission to study in the UK. Please visit the University's international student webpages for further information.
Those who have completed the Healthcare Data Science Micromasters via the EdX platform, which is the equivalent to one 15 credit module, will join the MSt at module 2.
If you have completed the Micromasters and would like to apply to the full MSt in Healthcare Data Science, please use the 'Ask a question' button on this page. We can then send you a link to the application form. All other applicants must apply via the ‘Apply now’ button on this webpage.
Teaching dates below are indicative and will be updated as soon as possible
The course is structured across the following modules:
Module 1 – Data driven decision making
This module explores data-driven decision-making in healthcare, focusing on project setup in Trusted Research Environments (TREs), data analysis, and reporting using RMarkdown to create dynamic, reproducible reports for healthcare insights. The module explores data-driven decision-making in healthcare, focusing on project setup in Trusted Research Environments (TREs), data analysis, and reporting using RMarkdown to create dynamic, reproducible reports for healthcare insights. Outcomes include a critical understanding of data-driven procedures, reproducible evaluation methods for diverse data sources, and advanced skills in appraising relevant literature. Students will gain a conceptual understanding of healthcare systems, patient pathways, legal/ethical data-sharing principles, and agile development processes, applying these concepts to real-world health data science projects.
- 3 October 2025 (online master class)
- 13-17 October (face to face)
- 7 November (online master class)
Module 2 – Principles of Health Data Science
This module introduces the foundational principles of Health Data Science, focusing on the complexities of accessing and working with patient-level data. It covers data access procedures and governance considerations required for patient level data. Students will develop professional skills in using reproducible tools and methodologies essential for conducting rigorous health data science projects, ensuring data integrity and compliance with industry standards.
- 28 November 2025 (online master class)
- 1-5 December (face to face)
- 19 December (online master class)
Module 3 – Health Data Science II
This advanced module builds on foundational Health Data Science concepts, focusing on data integration, cleaning, and preprocessing techniques essential for managing complex healthcare datasets. Students will develop comprehensive knowledge of data management strategies and best practices for creating effective project protocols, equipping them with the skills to handle diverse health data sources and ensure high-quality analysis and reproducibility in health data science projects.
- 9 January 2026 (online master class)
- 19-23 January 2026 (face to face)
- 6 February 2026 (online master class)
Module 4 – Data Visualisation
This module focuses on the principles and techniques of effective data visualization in health data science. Students will develop advanced skills in designing visual elements that communicate complex healthcare data insights clearly and effectively. The course emphasizes practical approaches to crafting visualizations that enhance understanding and decision-making in health data science projects, equipping students to convey complex concepts to diverse audiences.
- 6 March 2026 (online master class)
- 9-13 March (face to face)
- 20 March (online master class)
Module 5 – Machine learning
This module provides an in-depth exploration of Machine Learning (ML) approaches in the healthcare domain, emphasizing their role in developing and implementing health data science projects. Students will critically evaluate both human-based and automated ML techniques, assessing their effectiveness in real-world healthcare applications. The course covers key ethical considerations and the responsible use of ML in healthcare, fostering a comprehensive understanding of how these technologies can drive innovation while ensuring patient safety and data integrity.
- 3 April 2026 (online master class)
- 13-17 April (face to face)
- 1 May (online master class)
Module 6 – Databases
This module covers advanced concepts in database systems, focusing on data retrieval, management, and application in health data science. Students will gain expertise in using Structured Query Language (SQL) and other query languages to interrogate database servers. Key topics include database design standards and data linkage. The module also explores metadata development, data mining, and feature selection. By the end of the course, students will be able to translate complex clinical questions into effective SQL queries.
- 29 May 2026 (online master class)
- 8-12 June (face to face)
- 19 June (online master class)
Module 7 – Data analysis and inference
This module provides an in-depth understanding of data analysis and inference in the context of health data science. Students will develop professional skills in applying statistical and epidemiological principles to analyse health data, producing actionable insights. The course emphasizes a critical understanding of the scientific concepts that guide data interpretation and their implications for individuals and organizations. Additionally, students will evaluate the strengths and limitations of various statistical and epidemiological methods, analytical tools, and approaches, equipping them to make informed decisions in health data analysis.
- 2 October 2026 (online master class)
- 12-16 October (face to face)
- 6 November (online master class)
Module 8 – Advanced statistical methods
This module focuses on advanced statistical methods, providing students with the skills to evaluate and apply complex statistical techniques in health data science. It covers a wide range of advanced modelling approaches, enabling students to select and implement the most appropriate methods for specific scenarios. By the end of the module, students will demonstrate professional competence in using these advanced statistical tools, ensuring rigorous and robust analysis in health-related research and projects.
- 27 November 2026(online master class)
- 7-11 December (face to face)
- 18 December (online master class)
Module 9 – Research Dissertation
For dissertation projects students are able to choose from a range of healthcare data sources available to the programme and gain facilitated access to these data based on their research proposal to undertake their dissertation project, applying advanced analytical and data visualization techniques to large-scale health-related databases. Students will develop a systematic understanding of data-driven decision-making, focusing on reproducible approaches. The module emphasizes a deep conceptual understanding of the legal and ethical principles surrounding data sharing, equipping students to critically evaluate current methodologies and propose innovative approaches in health data research. Through this project, students will demonstrate their ability to formulate hypotheses and assess the suitability of these methods within a health organization context.
- 18-22 January 2027 (face to face)
- 23 April (online master class)
- 21 May (online master class)
Each module is worth the equivalent of 15 credits of study with the exception of the dissertation which is worth 60. 15 credits is approximately equivalent to 150 hours of study which will consist of face-to-face teaching, blended, and self-directed learning.
Supervision
Each learner will be assigned a dissertation supervisor who will be experienced in the area and/or methodology being studied as part of the dissertation. They will meet regularly during their dissertation development process with the supervisor, either in person or remotely. These meetings will support development of the research question and methodology, acquisition and analysis of data and feedback on a single draft of the dissertation.
Assessment
Each module (with the exception of the research dissertation) requires the submission of a piece of summatively assessed work which is between 2500-3000 words or equivalent.
The research dissertation of between 10-12,000 words.
Fees
The fees for 2025 entry will be £9,816 per annum for Home students and £19,632 per annum for overseas students. The combined graduate fee includes College membership.
For those continuing onto the MSt from the Micromasters, the overall fee is £17,751.25 for Home students and £35997.50 for Overseas fee payers.
Students will be expected to cover the application fee (£50 online) and any costs of travel, accommodation and subsistence during the course and sessions in Cambridge.
Funding
We do not currently have any scholarships or bursaries for this course. We recommend that you explore any potential funding well in advance of the application deadline. See our External funding page for more information.
Applicants are normally expected to a hold a 2i degree or higher from a UK university or an equivalent from an overseas university. It is preferred that an applicant's first degree be in a subject relevant, or related to, life sciences, medical sciences, computational or data science.
Required documents
In addition to the basic questions, applicants will be asked to provide:
- Academic transcripts
- Evidence of competence in English (if appropriate)
- Two references
- CV
Applications will be dealt with in two batches; any applications received up to the 30th April 2025 will be considered in batch one, and any applications received between the 1st and 31st May 2025 in batch two. We reserve the right to carry over applications received in the first batch over to the second batch. Interviews will be held approximately 2-3 weeks after each batch deadline.
For students who complete the Micromasters, you will also be required to provide evidence of successful completion of the Capstone assessment as part of your offer conditions.
When completing your application, please note the University restrictions and risks of using AI tools.
