See module specification for other years:
2022-232023-24
Module will run
Occurrence
Teaching period
A
Semester 1 2024-25
Module aims
Numerical and analytic skills are highly prized by employers, and provide graduates with a competitive edge in the job market, or when applying for future courses of study. This module will introduce a range of modern analysis techniques used in both academic and commercial domains. Concepts will be taught with reference to real examples and controversies, and practical sessions will give students hands on experience of implementing the techniques. Former students have gone on to varied and interesting careers in analytical roles, including working for the Government Statistical Service and the Joseph Rowntree Foundation.
Module learning outcomes
Give an overview of each data analysis method, including the key underlying theoretical assumptions
Compare analysis techniques and select an appropriate method for a given data set or experimental design
Design studies to take advantage of advanced analysis techniques
Implement some data analysis techniques in R (a software environment used for statistics)
Module content
Introduction to R
Meta-analysis and systematic reviews
Mixed effects models
Stochastic methods (bootstrapping)
Nonlinear curve fitting and optimization
Structural equation modelling
Multivariate Pattern Analysis
Fourier Analysis
Bayesian statistics
Indicative assessment
Task
% of module mark
Online Exam -less than 24hrs (Centrally scheduled)
100
Special assessment rules
None
Indicative reassessment
Task
% of module mark
Online Exam -less than 24hrs (Centrally scheduled)