This project provides Master's-level students in Statistics with an opportunity to carry out an independent piece of data analysis, and to present their investigation in the form of a dissertation.

Supervisory meetings to be arranged at certain fixed points, typically, weeks 2, 5/6 and 8/9, with additional weekly support sessions.

Interim assessment (20%, including a presentation and mini-viva) + dissertation (80%).

The aims of this course are:

■ to give students experience of working independently to analyse an extensive dataset in a discipline of interest to them;

■ to develop written and verbal presentation and communication skills.

By the end of this course students will be able to work independently, to:

■ Design an analysis plan to address the questions of interest about a data set;

■ identify relevant statistical methodology, if necessary, including methods not formally introduced in other components of their degree programme;

■ integrate the knowledge and skills they have gained from other components of their degree programme, and implement the data analysis in R, including exploratory data analysis, model fitting, model checking and comparison;

■ check the validity of the assumptions underpinning their chosen methods;

■ interpret results and draw appropriate conclusions from their analysis, including answering the questions of interest posed;

■ identify and explain clearly the limitations of the analysis and future work;

■ write up their results in the form of a report, in a well-structured, precise and clear manner;

■ defend their analysis and conclusions in a mini-viva.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.