Health Data Science and Clinical Informatics - MSc *
Currently viewing course to start in 2026/27 Entry.
The MSc Health Data Science and Clinical Informatics at Birmingham City University gives you the opportunity to become part of the next generation of professionals transforming healthcare through data.
- Level Postgraduate Taught
- Study mode Full Time/Part Time
- Award MSc
- Start date September 2026, January 2027
- Subjects
- Location City South
This course is:
Open to International Students
Overview
The MSc Health Data Science and Clinical Informatics at Birmingham City University gives you the opportunity to become part of the next generation of professionals transforming healthcare through data.
As the health sector embraces digital innovation, you’ll develop the skills to interpret and apply complex health data, supporting better decisions and improving patient outcomes. Working with experienced academics and industry professionals, you’ll explore how data, artificial intelligence, and informatics can shape the future of healthcare delivery. You’ll gain hands-on experience with real-world datasets, electronic health records, and machine learning tools, learning how to extract meaningful insights that inform clinical and public health practice.
The course combines expertise from health, computing, and engineering disciplines, creating an interdisciplinary learning environment that mirrors the realities of modern healthcare. Whether your background is in health, life sciences, computing, or engineering, you’ll be supported to develop the analytical, technical, and critical-thinking skills required to lead in this rapidly evolving field.
With access to specialist teaching, flexible study options, and research-informed learning, this course empowers you to make a real impact in digital health, the NHS, public health agencies, and the wider healthcare technology sector.
What's covered in this course?
You’ll build a strong foundation in health data science, epidemiology, and informatics, exploring how data is used to understand, predict, and improve health outcomes across populations and clinical settings.
Through practical sessions in R, Python, Git, GitHub and Lynx, you’ll learn to manage, clean, analyse, and visualise real-world health datasets, including electronic health records and biomedical data.
You’ll study machine learning, artificial intelligence, and data modelling to develop predictive tools and decision-support systems that inform clinical and policy decisions.
A module in health economics will help you understand how data-driven insights guide resource allocation and healthcare innovation. Interdisciplinary learning draws from public health, computer science, and biomedical engineering, preparing you to collaborate effectively across professional and academic domains.
The course concludes with an independent MSc dissertation project, where you’ll apply your learning to address a real-world healthcare data challenge, supported by expert supervision and professional guidance
This course could enhance my career by equipping me with in-demand skills to analyse health data, support clinical decisions, and improve healthcare systems.
Aarushi, student
Why Choose Us?
- Study in the Department of Life and Sports Sciences, School of Life and Health Sciences at Birmingham City University, a hub for cutting-edge research and teaching in health data, informatics, and digital healthcare innovation.
- Learn from research-active academics and industry professionals with real-world experience in biostatistics, epidemiology, health informatics, and machine learning, ensuring your learning is applied and current.
- Gain hands-on experience with R, Python, Lynx, and Git/GitHub, working with authentic health datasets to develop practical skills that are highly valued by employers.
- Benefit from collaboration with Computing, gaining exposure to advanced data science, software development, and machine learning expertise in real-world healthcare contexts.
- Engage in industry workshops, guest lectures, and applied projects with NHS, public health agencies, and digital health companies, enhancing your professional network and career readiness.
- Complete an independent MSc dissertation or applied research project, tackling real-world healthcare data challenges under expert supervision, preparing you for professional roles or further research.
OPEN DAY
Join us for an Open Day where you'll be able to learn about this course in detail, chat to students, explore our campus and tour accommodation.
Next Event: 15 November 2025
Entry Requirements
Essential requirements
Applicants are normally expected to have a minimum of a 2:2 honours degree in a relevant subject such as Life or Health Sciences, Computer Science, Informatics, Statistics, or related disciplines.
If you have a qualification that is not listed, please contact us.
Fees & How to Apply
UK students
Annual and modular tuition fees shown are applicable to the first year of study. The University reserves the right to increase fees for subsequent years of study in line with increases in inflation (capped at 5%) or to reflect changes in Government funding policies or changes agreed by Parliament. View fees for continuing students.
Award: MSc
Starting: Sep 2026
- Mode
- Duration
- Fees
- Full Time
- 12 months
-
TBC
- Register interest
- Part Time
- 24 months
-
TBC
- Register interest
Award: MSc
Starting: Jan 2027
- Mode
- Duration
- Fees
- Full Time
- 12 months
-
TBC
- Register interest
- Part Time
- 24 months
-
TBC
- Register interest
International students
Annual and modular tuition fees shown are applicable to the first year of study. The University reserves the right to increase fees for subsequent years of study in line with increases in inflation (capped at 5%) or to reflect changes in Government funding policies or changes agreed by Parliament. View fees for continuing students.
Award: MSc
Starting: Sep 2026
- Mode
- Duration
- Fees
- Full Time
- 12 months
-
TBC
- Register interest
Award: MSc
Starting: Jan 2027
- Mode
- Duration
- Fees
- Full Time
- 12 months
-
TBC
- Register interest
Personal statement
You’ll need to submit a personal statement as part of your application for this course. This will need to highlight your passion for postgraduate study – and your chosen course – as well as your personal skills and experience, academic success, and any other factors that will support your application for further study.
If you are applying for a stand alone module, please include the title of the module you want to study in your Personal Statement.
Not sure what to include? We’re here to help – take a look at our top tips for writing personal statements and download our free postgraduate personal statement guide for further advice and examples from real students.
Course in Depth
Modules
In order to complete this course a student must successfully complete all the following CORE modules (totalling 140 credits):
This module provides a foundation in health data science and epidemiology, introducing core concepts, study designs, and statistical reasoning. Students engage in practical workshops using R and Python to explore real-world datasets, fostering critical thinking and applied analytical skills essential for public health and clinical research.
Building on core principles, this module focuses on research methodology, data collection, and analysis. Students undertake hands-on exercises, developing reproducible workflows with Git/GitHub and applying statistical methods to evaluate health outcomes, promoting evidence-based decision-making and critical appraisal of research.
This module explores digital health systems, electronic health records, and health information governance. Students gain practical experience in managing, integrating, and interpreting complex clinical and public health datasets, while considering ethical, legal, and regulatory frameworks that underpin responsible data use.
Students learn advanced data modelling, predictive analytics, and visualisation techniques. Practical workshops using R, Python, and machine learning libraries enable the application of computational methods to real-world health datasets, supporting decision-making and problem-solving in clinical and public health contexts.
The dissertation provides an opportunity for independent, applied research. Students design and execute a project integrating statistical, computational, and domain-specific knowledge, supervised one-to-one to ensure rigorous methodology, ethical compliance, and high-quality analysis and reporting.
In order to complete this course a student must successfully complete at least 40 credits from the following indicative list of OPTIONAL modules:
Examines economic evaluation and resource allocation in healthcare, supporting evidence-based policy decisions.
Develops advanced analytical techniques, including regression, survival analysis, and study design.
Focuses on computational approaches to large health datasets using machine learning tools and predictive modelling.
Introduces analysis and interpretation of imaging and sensor-derived biomedical data, emphasising translational applications.
How you'll learn
On the MSc Health Data Science and Clinical Informatics, you will develop applied skills and analytical thinking through hands-on engagement with real-world health data.
Level 7 – Semester 1
You will begin by building a strong foundation in health data science, epidemiology, and research methods. Structured lectures, interactive workshops, and practical sessions using R, Python, Lynx, and Git/GitHub introduce computational and statistical tools, reproducible workflows, and ethical data practices. Small group seminars support critical reflection, problem-solving, and collaborative learning, enabling you to communicate complex data insights effectively.
Semester 2
Learning focuses on advanced analytical and computational methods, including machine learning, health data modelling, biomedical sensing, and digital health tools. You will apply knowledge to case studies, scenario-based learning, and applied projects, often collaborating with the Department of Computing to gain exposure to software development and advanced data science expertise. These activities develop problem-solving, teamwork, and professional decision-making skills.
Dissertation / Applied Research Project – Semester 2/3
The course culminates in independent research or applied project, allowing you to integrate theoretical and practical skills to address real-world challenges. One-to-one supervision ensures guidance on study design, data ethics, analysis, and reporting.
Flexible online learning via Moodle/Cadmus complements face-to-face teaching, supporting self-directed study and consolidation of knowledge. Regular formative feedback, feedforward strategies, and practical workshops ensure students are supported to achieve their potential and develop the skills required for a career in digital health and clinical data science.
Employability
Enhancing employability skills
This MSc equips you with the technical, analytical, and professional skills essential for careers in health data science, digital health, and clinical informatics. You will gain hands-on experience working with real-world health datasets, applying advanced statistical, computational, and machine learning techniques using R, and Python.
Specifically, you will learn to:
- Apply epidemiological, statistical, and data science methods to analyse complex health datasets.
- Integrate theoretical knowledge with practical problem-solving to inform public health, clinical, and policy decisions.
- Work independently and collaboratively in interdisciplinary teams, communicating insights to technical and non-technical audiences.
- Manage projects, plan analyses, and present findings to a professional standard.
- Develop critical thinking, ethical reasoning, and reflective practice to support lifelong learning in a rapidly evolving digital health landscape.
The programme also provides professional guidance in career development, including CV preparation, interview techniques, and networking strategies. You will learn to present data-driven insights effectively through reports, dashboards, and visualisation tools, enhancing your ability to influence decision-making in healthcare and research settings.
By combining applied data skills with professional practice, this MSc prepares graduates for a wide range of roles across the NHS, public health agencies, digital health companies, and the pharmaceutical sector. The emphasis on reproducible workflows, Git/GitHub, and interdisciplinary collaboration ensures you are work-ready and equipped to make an immediate impact in health data-driven roles.
Placements
Although the course does not include a formal placement, our collaboration with Health Data Research UK (HDR UK) provides students with access to the Health Data Science Black Internship Programme, an eight-week paid placement, fully facilitated by HDR UK.
Graduate jobs
Graduates from the MSc Health Data Science and Clinical Informatics are well-prepared for a wide range of roles in the UK and internationally. Typical career paths include:
- Health Data Analyst or Scientist roles within the NHS, public health agencies, or local authorities
- Clinical or biomedical data specialist positions in pharmaceutical, biotechnology, and digital health companies
- Health informatics and digital health roles, including decision support, predictive modelling, and AI applications in healthcare
- Research assistant or project roles in academic, government, or non-profit health research organisations
- Opportunities in health technology assessment, policy analysis, and data-driven consultancy
Links to industry
The MSc Health Data Science and Clinical Informatics benefits from strong partnerships with regional, national, and international organisations, ensuring your learning is closely linked to professional practice. These collaborations provide opportunities for applied learning, guest lectures, workshops, and insight into real-world health data challenges.
Regional: Partnerships with Birmingham City Council, University Hospitals Birmingham NHS Foundation Trust, and the Birmingham and Solihull Integrated Care System, as well as the NHS Strategy Unit and Nonacus, allow students to engage with live datasets, policy initiatives, and digital health projects.
National: Engagement with organisations such as Health Data Research UK (HDR UK) provides access to initiatives including the Health Data Science Black Internship Programme, giving students exposure to applied research and professional development opportunities.
International: Collaborative networks with leading global health data and digital health institutions offer insight into international standards, emerging technologies, and interdisciplinary approaches to healthcare analytics.
These links ensure students gain applied experience, develop professional networks, and understand how health data science is used to inform policy, research, and clinical decision-making.
Facilities & Staff
Students on this MSc have access to modern computer labs and lecture theatres, supported by Lapsafe systems and extensive library resources, including online journals and datasets. Practical sessions use widely available statistical and data science software, such as Python and R, as well as platforms like Jupyter Notebooks and Google Colaboratory, enabling students to apply their learning to real-world health data both on-campus and remotely.
Our staff
Dr Feroz Jadhakhan
Lecturer in Public Health
Dr. Feroz is passionate about using electronic healthcare records to identify disease trends and patterns, providing valuable insights into population health, epidemiological patterns, and intervention effectiveness. His analysis of health data enables early disease detection, chronic condition monitoring, and identification of high-risk...
More about FerozDr George Oguntala
Senior Lecturer in Biomedical Engineering - Course Leader for MSc Medical Imaging Technology
George is a Senior Lecturer in Biomedical Engineering with specialism in Smart Homes and Infrastructures, Internet of Things, Sensors and Wearable Electronics at the Department of Life Sciences, Birmingham City University. He is the Programme Leader for MSc in Medical Imaging Technology Courses under the Biomedical Engineering provision at...
More about GeorgeDr Faizan Ahmad
Course Leader in Biomedical Engineering
Dr Faizan Ahmad possesses experience in the multidisciplinary fields of engineering within the industry and academia. He has worked in different roles in mechanical and biomedical engineering disciplines, with a primary emphasis on engineering, biomedical and soft tissue material mechanics. He served as a design, stress, manufacturing, quality...
More about FaizanDr Yunpeng Jia
Assistant Lecturer
Dr. Yunpeng Jia holds a Bachelor of Science in Applied Physics from Chongqing University where he completed his final project under the supervision of Professor Yingzhou Huang. Following this, he earned a Master of Science in Optical Fibre Technologies, with distinction, from the University of Southampton, UK, under the guidance of Dr. Peter...
More about YunpengDr Vivek Indramohan
Associate Professor and Course Lead - Biomedical Engineering
With an overseas research student award (ORSAS) and University of Strathclyde scholarship, Vivek completed his Ph.D. (in Bioengineering) in 2009. Following the completion of his research degree, he commenced his work as a Research Assistant at University College of London (UCL) for 6 months, during which he was successful in obtaining a...
More about Vivek