Online Genomic Medicine with Data Science MSc
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Course overview
Informed by industry experts and designed by leading academics, this online Masters in Genomic Medicine with Data Science has been developed to upskill and support your career progression.
There is a growing demand across the pharmaceutical, life and health science for skilled graduates with both biological knowledge and the computational and analytical interest to drive genomic precision medicine. From early diagnosis, to drugs based on our unique genetic codes, to disease prevention, this course aims to equip you with the skills needed to make a real-world impact.
Aimed at both professionals and graduates with a biological or medical background, you will gain the skills to use large volumes of complex data, encompassing genomics, proteomics, metabolomics, phenotypic data, epidemiology and clinical trial investigations, to improve the understanding of disease mechanisms.
This will include the use of bioinformatic tools and statistical principles and methods that are foundational to artificial intelligence.
Our online course is accessible from anywhere in the world, for study when it suits you. You will be able to plan your studies around your job, family life and other commitments, and take breaks between modules if needed.
Course details
- Start date: September 2025 (Induction in August) and March 2026 (Induction in February)
- Delivery type: online exclusive
- Duration: 24 months part time
- Entry requirements: Applications will be individually assessed based on the evidence of educational and work experience provided. You should meet one of the following requirements:
- A 2.1 (hons) bachelor degree or equivalent, in a relevant scientific discipline related to biological or natural sciences.
- A 2.2 (hons) bachelor degree or equivalent, in a relevant scientific discipline related to biological or natural sciences, with a minimum of 2 years’ relevant work experience.
- A 2.2 (hons) degree or equivalent, in any subject with a minimum of 3 years’ relevant experience.
- English language requirements: IELTS 6.5 overall with no less than 6.0 in any component
- Fees (UK and international): £13,000 (total). Fees can be paid on a module-by-module basis.
Module details
The following list represents typical modules/components studied and may change from time to time. Read more in our terms and conditions.
Year 1 compulsory modules
Programming for Data Science
Build a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.
High-Throughput Technologies
Gain an understanding of the use of high-throughput biomolecular data generation methods. The emphasis will be on understanding methods and the data that they typically give. Techniques covered will include whole genome/exome sequencing, gene expression, RNA-seq and epigenetics, proteomics, chemical proteomics, high-throughput RNA biology, single-cell methods and metabolomics.
Statistical Methods
Undertake a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.
Year 1 optional modules
Data Science
Understand methods of analysis that allow you to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.
Analytical Skills in Precision Medicine
Evaluate and use DNA and protein sequences and structure. The emphasis will be on the use of computational tools to gain information about genes and variants, their function and associations with disease, as well as predicting protein structure. Computational biology databases and tools that aid in the interpretation and understanding of biomedical research results and their placement within the wider context of the field will be explored.
Genetic Epidemiology
Take an introduction to genetic epidemiology covering the main topics of current interest in the field. An introduction to human genetics will be included, but the main emphasis is on understanding statistical and epidemiological aspects of the study of the genetic basis of human diseases.
Clinical Trials
Understand the principles of clinical trial design, conduct, analysis and reporting. The emphasis will be on understanding the practical issues that arise through real examples backed up with the relevant theory. It will provide a grounding in the basic specialist knowledge and skills required by a non-statistician working on phase II/III clinical trials.
Big Data: Rare and Common Disorders
Gain insight into the way big data is impacting our understanding of human disease and the development of therapies. It will focus on a variety of disorders ranging from rare Mendelian disease to common disorders of complex aetiology.
Statistical Learning
Statistical learning is at the core of the modern world. Online advertising, automated vehicles, stock market trading, transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It is how we translate data into knowledge.
Year 2 compulsory modules
Cancer Drug Development
Focus on the challenges and latest developments in anti-cancer drug development from a pharmacological point of view. The aim is to provide a good understanding of the processes and difficulties in successfully translating anti-cancer research into clinical practice to improve patient outcomes.
Extended Data Analysis Topics
This module will give you an opportunity to analyse large datasets in an independent manner in order to answer a research question of their design. This will cover analysis of a range of data types and analytical techniques, for example germline DNA or RNA sequencing.
Year 2 optional modules
Data Science
Understand methods of analysis that allow you to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.
Analytical Skills in Precision Medicine
Evaluate and use DNA and protein sequences and structure. The emphasis will be on the use of computational tools to gain information about genes and variants, their function and associations with disease, as well as predicting protein structure. Computational biology databases and tools that aid in the interpretation and understanding of biomedical research results and their placement within the wider context of the field will be explored.
Genetic Epidemiology
Take an introduction to genetic epidemiology covering the main topics of current interest in the field. An introduction to human genetics will be included, but the main emphasis is on understanding statistical and epidemiological aspects of the study of the genetic basis of human diseases.
Clinical Trials
Understand the principles of clinical trial design, conduct, analysis and reporting. The emphasis will be on understanding the practical issues that arise through real examples backed up with the relevant theory. It will provide a grounding in the basic specialist knowledge and skills required by a non-statistician working on phase II/III clinical trials.
Big Data: Rare and Common Disorders
Gain insight into the way big data is impacting our understanding of human disease and the development of therapies. It will focus on a variety of disorders ranging from rare Mendelian disease to common disorders of complex aetiology.
Statistical Learning
Statistical learning is at the core of the modern world. Online advertising, automated vehicles, stock market trading, transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It is how we translate data into knowledge.
Your future
As outlined in the recent publication ‘Science Industry Partnership: Skills Strategy 2025’, there is a demand amongst employers for the skills that you will develop on this programme.
Students who have studied our modules have gone on to a variety of roles such:
- Computational biology
- Data analytics functions
- Clinical genetics
- Clinical Scientist
- Clinical leadership in genomic medicine
- Data validation coordinator
- Public health intelligence analyst
- Bioinformatician
Why study Genomic Medicine with Data Science at Leeds?
Our programme has been developed in consultation with our Industrial Advisor Board (IAB), including organisations such as GSK, AstraZeneca, LabCorp and NHS representatives. This ensures it aligns closely with industry needs.
The University of Leeds is now a partner of The Alan Turing Institute, the UK’s prestigious national institute for data science. The partnership is spearheaded by the Leeds Institute for Data Analytics (LIDA).
Revolutionise medicine online: Learn more about Genomic Medicine with Data Science
Our experts
Mark Iles, one of the experts who teach on this course, has more than 25 years of experience in Genetic Epidemiology.

Right now, the idea of using genetics as a routine part of clinical practice is just starting to open up. It’s an exciting time!