Fees and funding

Fees

For entry in the academic year beginning September 2027, the tuition fees are as follows:

  • MRes (full-time)
    UK students (per annum): £15,400
    International, including EU, students (per annum): £35,700

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 .

Policy on additional costs

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).

Course unit details:
Exploring and Modelling Data with R

Course unit fact file
Unit code PCHN63101
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Division of Psychology Communication and Human Neu
Available as a free choice unit? No

Overview

The topics covered will include:

  • Basic principles of coding in R
  • Data wrangling and data visualisation
  • Producing reproducible reports in RMarkdown
  • Implementing and interpreting statistical models in R under the General Linear Model framework 

Aims

  • To familiarise students with a range of advanced, quantitative analytical techniques at a level not normally encountered in undergraduate study.
  • To equip students with the confidence and skills necessary to apply the methods to datasets using R.
  • To provide sufficient understanding for sophisticated statistical decision-making and interpretation of results. 

Learning outcomes

Having attended the unit, students will be able to:  

  • Write clear and functional code in base R to import, clean, transform, summarise, and visualise data.  
  • Produce reproducible analytical reports using R Markdown that integrate code, output, and narrative.  
  • Explain the logic of statistical modelling within the General Linear Model (GLM) framework, including assumptions, parameter estimation, and inference.  
  • Implement GLM-based analyses ranging from simple linear regression to multi-factor ANOVA using R. 
  • Interpret model outputs, check assumptions and conduct follow-up tests 

Teaching and learning methods

In Semester 1 there will be weekly 2-hour seminars providing an introduction and explanation of each technique and practical training. Each technique will be demonstrated using R or other appropriate software.   

Assessment methods

Assessment task

How and when feedback is provided

Weighting within unit (if relevant)

Continuous assessment. Two assignments.

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

Data Wrangling and Visualisation: The form of assessment varies from year to year. You will be required to import, clean, transform, and summarise a dataset that will be provided, and to create appropriate visualisations using R. The assignment will assess good practice in writing base R code. Your report will be produced in R Markdown, integrating code, output, and interpretation.

ANOVA: the form of assessment varies from year to year. You will analyse and discuss data sets provided to you. You will be asked to carry out the appropriate analyses and for each dataset, write a results section (where you only report on descriptive and inferential statistics) and a brief discussion section (where you interpret the results, based on the analyses you carried out). Your report will be written in R Markdown.   

 

 

 

As per feedback policy

 

50%

 

 

 

50%  

Recommended reading

Appropriate online R-based resources will be made available alongside each lecture. 

Study hours

Scheduled activity hours
Lectures 22
Independent study hours
Independent study 128

Teaching staff

Staff member Role
George Farmer Unit coordinator

Return to course details

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