MRes Experimental Psychology with Data Science

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MRes (full-time)
  UK students (per annum): £14,700
  International, including EU, students (per annum): £34,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

The topics covered will include:

• Using Conda to create reproducible Python environments.
• Programming in Python using Jupyter Notebooks.
• Data analysis using the NumPy, pandas (and related) Python libraries.
• How to use git and GitHub for version control and collaborative working.
• Making your analyses reproducible using Docker.
• Understanding the principles of statistical/machine learning.
• Classification and resampling methods.
• Model selection and regularization.
• Unsupervised statistical learning.  

• To equip students with a range of advanced computational and analytical techniques.
• To equip students with the confidence and skills necessary to apply the methods to datasets using Python.
• To provide sufficient understanding for sophisticated statistical decision making and interpretation of results.
• To contextualise statistical analysis within the principles of reproducibility and Open Research.

Having attended the unit, students will be able to:

• Conduct data analyses in Python.
• Demonstrate their knowledge and skills required for open and reproducible science.
• Demonstrate their ability to understand the principles of statistical/machine learning.
• Demonstrate their ability to apply statistical learning methods to different datasets. 

The course will be taught through a combination of synchronous lectures and lab sessions, alongside online asynchronous teaching materials. Additional lab sessions and lab support will be available for students, if needed. Teaching will be complemented by the availability of notes, slides and recommended reading.  

Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this module

One assessment will be on programming and data analysis using Python and the other assessment will be on applying statistical learning techniques to existing data 

Appropriate online resources will be made available alongside each lecture.