Taught course

Master of Data Science (Bioinformatics and Biological Modelling)

Durham University · Faculty of Science

Entry requirements

A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences. Candidates with a degree in Biological or Physical sciences are strongly encouraged to apply.

Months of entry


Course content

From security cameras and satellite transmission to our mobile devices, homes, cars, and in the workplace, we are surrounded by data. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight about 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 and humanities.

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 course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as neural networks, bioinformatics and deep learning, to expand your knowledge and understanding. Optional modules allow you to focus on an area of interest.

The MDS culminates in the research project, an in-depth investigation into an area of specific interest in which you apply the skills you’ve learned during the course to a research problem in biosciences.

Course Structure

The Data Science Research Project is a substantial piece of 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 develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. 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 and Bias in Data Analytics 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. 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, neural networks and deep learning.

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.

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

Fees and funding

UK students
International students

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification

Course contact details

Recruitment and Admissions