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Degree type

MDS

Course length

1 year full-time

Location

Durham City

Programme code

G5P223

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Course details

Combine data science with life sciences to tackle complex biological questions. This conversion course equips you with the skills to analyse genomic, molecular, and ecological data using advanced modelling and machine learning techniques preparing you for impactful careers in research, healthcare, and biotechnology.

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment. This has led to a significant increase in demand for skilled data scientists, and this demand is predicted to further grow.  

Drawing on this, we have created the Master of Data Science (Bioinformatics and Biological Modelling), a conversion course that equips you with the skills to access, clean, analyse, and visualise data, opening a future in data science even if your first degree doesn’t include a strong data component. It is likely to appeal to those with a background in biological or physical sciences. 

The MDS provides training in contemporary data science, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses build wider skills in statistical and machine learning, while subject-specific modules will develop your quantitative skills in bioinformatics and biological modelling. It is equally suitable whether you are planning to use quantitative analysis in a research capacity in molecular biology, or if you are a physical or biological science graduate who wants to learn transferrable data and modelling analysis skills. 

The MDS culminates in the research project, an in-depth investigation in which you apply the skills learned during the course to a research problem working alongside an expert in the area of application of your choice.  

The course begins with a range of introductory modules before progressing to more advanced contemporary techniques in machine learning to expand your knowledge and understanding. We offer an extensive range of optional modules which allows you to focus on an area of interest such as text analytics and data visualization. Optional modules allow you to focus on an area of interest.

Course structure

Year 1 modules

Core modules:

The Data Science Research Project  

is a substantial piece of self directed research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills. 

Critical Perspectives in Data Science and AI 

develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically utilising AI applications. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project. 

Bioinformatics

provides you with a broad understanding of the field of bioinformatics as well as the R environment for data analysis and visualisation in bioinformatics. You will also learn to analyse genomic and transcriptomic data, DNA and protein sequence data, and develop the skills to use public bioinformatics databases.

Programming for Data Science

uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.

Ethics of Artificial Intelligence and Data Science 
 

introduces contemporary debates on ethical issues and bias resulting from the application of data analytics, statistical modelling and artificial intelligence in society. You will learn about contemporary philosophical research on these issues and how to apply this research in practice. The module includes an essay about an ethical topic, completed under the guidance of a tutor. 

Machine Learning

introduces the essential knowledge and skills required in machine learning for data science using the R statistical language. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, and neural networks.

Introduction to Statistics for Data Science

focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.

Optional modules:

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

  • Modelling in Molecular Biology
  • Strategic Leadership
  • Introduction to Mathematics for Data Science
  • Introduction to Computing for Data Science
  • Text Mining and Language Analytics
  • Data Exploration, Visualisation and Unsupervised Learning

Learning

This interdisciplinary course is made up of modules that span across the University. It incorporates a wide range of learning and teaching methods , these include lectures, seminars, workshops and computer/practical classes. The taught elements are further reinforced through independent study, research and analysis, case studies and structured reading. 

All modules are underpinned by research and embed elements of research training in both delivery and assessment. Throughout the course you will be encouraged to develop research methods, skills and ethics reflecting the methods used by the research-active staff. Overall, you will be encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for use in future work or research. 

Assessment

The Master of Data Science (Bioinformatics and Biological Modelling) is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups. 

The course culminates with a major research project, which is conducted and written up as a self-directed piece of work with support from your appointed supervisor.  

Entry requirements

We require a 2:1 Bachelor (Honours) degree or international equivalent in any degree subject.

Applicants with a strong data science or mathematical background may wish to consider our MSc Scientific Computing and Data Analysis programmes.

Alternative qualifications

International students who do not meet direct entry requirements for this degree might have the option to complete an International Foundation Year.

Home students who do not meet our direct entry requirements, may be eligible for our Foundation Programme which offers multidisciplinary programmes to prepare you for a range of specified degree programmes.  

English language requirements

Country specific information

Fees and funding

The fees for this academic year have not been confirmed yet.

The tuition fees shown are for one complete academic year of study and are set according to the academic year of entry. Fees will be subject to an annual inflationary increase and are expected to rise throughout the programme of study. The fee listed above is for the first year of the course only.
 
More information is available here: Tuition fees - how much are they - Durham University
 

Please also check costs for colleges and accommodation.

Scholarships and Bursaries

We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities. 

Find out more about Scholarships and Bursaries

Career opportunities

Natural Sciences

In today’s data-driven world, the ability to capture, analyse, and communicate insights from complex information is one of the most sought-after skills by employers globally.

Graduates are equipped for exciting and meaningful careers across a wide range of sectors from science, health, and the environment to the humanities and social policy where data is driving innovation and transforming how we live and work. Whether you're influencing public health strategies, advancing climate research, or unlocking cultural insights, your data science expertise will open doors to impactful opportunities.

Department information

Natural Sciences

Our Master of Data Science programmes give you the tools to capture, analyse, and unlock insights from the vast streams of data shaping our world. These interdisciplinary degrees empower smarter decision-making across sectors from health and environment to culture and society.

The Master of Data Science programmes at Durham University equip you with the skills to capture, process, and analyse the vast and complex data shaping every aspect of modern life. From healthcare and environmental science to social policy and cultural heritage, data science is transforming how decisions are made and problems are solved.

The suite of programmes are designed as conversion degrees, ideal for students from non-data science backgrounds who want to transition into this high-impact field. You'll gain a strong foundation in data science while exploring its applications across a range of disciplines.

Choose from seven pathways tailored to your interests and career goals: the Master of Data Science or Master of Data Science with AI Applications, or specialist routes in Bioinformatics and Biological Modelling, Digital Humanities, Earth and Environment, Health, Social Analytics, and Heritage.

As a student, you’ll benefit from Durham’s interdisciplinary approach and the expertise of the Institute for Data Science, a hub for innovation that supports research and teaching across the University. Join a vibrant academic community working to transform nature, society, and culture through data.

For more information see our department pages.

Facilities

Data Science is a conversion course that incorporates content from many Departments across the University.


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Apply for a postgraduate course (including PGCE International) via our online portal.  

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