BSc Module – Data Science for Neuroscience

Starting 2027

Data Science for Neuroscience is a Level 5 undergraduate module designed for students who want to become more confident working with real biological and neuroscience data. Modern neuroscience increasingly depends on the ability to handle complex datasets, interpret data critically, and communicate findings clearly. This module is intended to give students a practical introduction to those skills in a way that is grounded in real research questions rather than abstract theory alone.

The emphasis is on learning by doing. Students will work through examples drawn from neuroscience, develop confidence with common approaches to data analysis and visualisation, and build a stronger foundation for reading research papers, undertaking projects, and engaging with quantitative methods across the rest of their degree.

This module is especially suitable for students who are curious about research, want to strengthen their practical and analytical skills, or feel they need a more solid grounding in the data side of modern neuroscience.

Please note: this page is an informal overview for prospective students. Official module information, including final timetabling and assessment details, will be provided through KEATS.

What is the module about?

The module introduces students to core ideas in data science through the lens of neuroscience. It focuses on how data are collected, structured, analysed, visualised, and interpreted, and how these choices shape scientific conclusions.

Rather than treating “data science” as something separate from biology, the module explores how quantitative approaches help us make sense of real problems in neuroscience.

Topics likely to include

• different types of neuroscience and biological data

• dimensionality reduction

• clustering and classification

• predictive modelling 

• machine learning basics 

• visual representation of data 

What skills will students develop?

By the end of the module, students should be better able to:

• understand and classify different kinds of data

• work with real-world datasets

• apply basic analytical and statistical approaches

• present data clearly and effectively

• think critically about how data are used in neuroscience research

• discuss ethical issues around data-driven approaches

How is it taught?

The module combines lectures with computer-based practical sessions and guided independent work. The goal is not just to explain concepts, but to help students practise them in a structured, supportive way.

Students should expect a mixture of:

• short conceptual teaching

• practical exercises

• discussion of examples from neuroscience research

• independent work developing confidence with methods introduced in class

Who is the module for?

This module is aimed at undergraduate neuroscience and bioscience students who want to build practical quantitative skills. It is a good fit for students who are interested in research, data, or the analytical side of neuroscience, but it is not only for students who already see themselves as “technical”.

Frequently asked questions

Do I need to be able to code already?

No. The module is intended to help students build confidence step by step. Prior experience may be helpful, but it is not the main expectation. Curiosity, willingness to practise, and readiness to engage with unfamiliar material matter more.

Is this a very maths-heavy module?

It is quantitative, but the aim is not to make the module feel like pure mathematics. The focus is on understanding data in a practical and applied way. Students will encounter statistics and modelling, but these are taught as tools for answering neuroscience questions.

Is it more theoretical or practical?

It is deliberately a mixture of both, with a strong practical emphasis. Students will learn key ideas, but they will also spend time applying them to examples and datasets.

What kind of data will we look at?

The module uses neuroscience as its main context, so students should expect examples based on real biological or neuroscience datasets. The exact examples may vary, but the overall goal is to show how modern neuroscience makes use of data science methods in practice.

What will I get out of it?

Students who engage well with the module should come away with stronger confidence in handling data, interpreting figures and analyses in papers, and approaching research questions in a more structured and quantitative way. These are useful skills not only for further study and research projects, but also more broadly across scientific and professional contexts.

Is this a good module if I am considering a project or research career?

Yes. It is particularly useful for students who may go on to undertake an independent project, lab-based research, or any work involving data analysis. It is also valuable for students who simply want to feel more confident reading and evaluating scientific literature.

What is the workload like?

As with most 15-credit modules, students should expect regular engagement across the term, including taught sessions and independent study. The module rewards steady practice more than last-minute revision.

I’m interested, but a bit nervous about the “data science” label. Should I still consider it?

Probably yes. Many students are put off by the label more than by the actual content. This module is meant to help students grow into the subject, not to assume that they already feel expert or highly technical.