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.
Course Dates
Course details
Tutors
Key course information
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.
Watch the information session for MSt in Healthcare Data Science from our Medical Open Week 2025.
Who is the course designed for?
The MSt in Healthcare Data Science is designed for professionals and aspiring leaders who want to advance their careers at the interface of health and data science. It is particularly suitable for:
- healthcare professionals (e.g. clinicians, nurses, allied health staff, public health practitioners) who would like to enhance their technical, coding and data science skills to develop and apply data-driven approaches to improve patient care and healthcare systems
- data scientists, statisticians, and computational scientists seeking to specialise in healthcare applications and gain a deeper understanding of the medical, behavioural, and organisational contexts in which their work will be applied
- researchers and academics in medicine, biomedical sciences, or related fields who want to strengthen their quantitative and technical skills for health research
- policy makers, managers, and professionals in health organisations who require a robust understanding of health data analytics to inform evidence-based decisions, design interventions, and manage health services
- industry professionals (e.g., in biotechnology, pharmaceuticals, digital health, or medtech) who are looking to bridge scientific, technical, and healthcare knowledge to develop and deliver innovative solutions
This course will appeal to individuals who are:
- motivated to combine mathematical, statistical, computational, and health sciences in a multidisciplinary way
- eager to develop both technical expertise and leadership skills to contribute effectively to data science projects in health
- working professionals seeking a flexible, part-time programme that can fit alongside their careers
- committed to advancing their impact in healthcare through innovation, evidence, and collaboration
Aims of the programme
The course will:
- provide teaching and learning opportunities to gain the knowledge and skills that underpin and 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.
We welcome applications from students with a variety of backgrounds and experiences. As part of our admissions process, you’ll need to meet certain requirements and make sure you’re able to attend teaching sessions in the UK.
Standard entry requirements
Typically, we expect a good UK undergraduate degree, such as a 2.1, or international equivalent. 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.
If your degree is not from the UK, check international qualifications on the University’s postgraduate site to find the equivalent in your country.
English language requirements
Our courses are taught in English and require a good level of fluency. If English is not your first language, you'll need to prove you have sufficient fluency before admission. If we offer you a place, it will be subject to you meeting this requirement. For more information, visit Postgraduate and Master's admissions and the University’s English language requirements.
Visa information
We welcome applications from international students. If you’re coming from overseas, you would attend the in-person teaching sessions for this course with visitor immigration permission.
It's important to be aware that entering the UK as a visitor for study purposes comes with certain expectations and restrictions. To make sure you understand the requirements, we advise you to read the in-depth information on the University’s International Students website.
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 in-person sessions requiring attendance in Cambridge (blended with remote learning where suitable), plus self-directed learning supported through a Virtual Learning Environment (VLE).
Full in-person attendance is required at the teaching blocks commencing October 2026. The Master class sessions will take place online.
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.
Below are the expected teaching dates for this course. If they change, we'll update offer-holders in line with the University's terms of admission. Exact teaching dates will be added to this page in due course.
Teaching
Year 1/Year 2
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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 2,500 to 3000 words or equivalent.
The research dissertation is 10,000 to 12,000 words.
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 in-person teaching, blended, and self-directed learning.
Fees
The total fees for this course are shown above in 'Course details'.
For those continuing onto the MSt from the Micromasters, the fee per year is £8,751 for home students and £17,502 for overseas students.
To help you manage your finances more comfortably, you can pay the fee in instalments. See how to pay for more.
There are also some additional costs you’ll need to cover as part of this course. These are usually:
- an application fee of £85, unless you're eligible for a fee waiver, payable online
- any travel, accommodation and subsistence costs for the residential teaching sessions held in Cambridge
Funding
We're dedicated to reducing and removing financial barriers to learning. Visit financial support ahead of the application deadline to find out what options may be available to help you in your studies. You can explore external funding and stay up to date on our concessions and bursaries.
Considering applying? We look forward to receiving your application.
Applications will be considered on a rolling basis. Should the course become full, we reserve the right to close for applications early. We encourage applicants to apply as soon as possible.
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.
Key timings for your application
- The application deadline is the 28 May 2026.
- Applications will be considered on a rolling basis with interviews taking place from March 2026.
- If you're shortlisted, we'll contact you in the weeks before interviews to arrange a time and format for yours.
How to apply and what you'll need
The ‘Apply now’ button will take you to the Applicant Portal. There, you can:
- create, save, and submit your application
- upload your supporting documents
- submit and manage your references
- pay your application fee
- track your application
Supporting documents
When you submit your application, you’ll need to provide supporting information.
CV
Upload an up-to-date resume.
Qualifications and transcripts
Upload details of degree-level courses you have completed or are studying.
References
Submit contact details for two referees, who we will contact on your behalf.
Capstone assessment
For students continuing from the EdX Micromasters, you will also be required to provide evidence of successful completion of the Capstone assessment as part of your offer conditions.
For more information on applying and admissions, see Postgraduate and Master's admissions.
As an MSt student, you'll become a member of a Cambridge College. For the MSt in Healthcare Data Science, our students were members of Homerton in 2025-26. College membership for 2026 entry will be confirmed here shortly.
To find out more about College membership, watch the ‘Meet the Colleges’ recording from our Master's Open Week 2024.
If you have a pre-existing membership at a Cambridge College other than the College we intend to send the cohort to, you can ask them to consider you as a member for this course. However, we cannot arrange this for you. If you do not have a pre-existing College membership, you can only become a member of our partner College.
We're committed to supporting you in your learning journey, and we offer a variety of support opportunities to meet individual needs. Visit student support to find out more about how we can help.
