Module code: SPA5131
Credits: 15.0
Semester: SEM2
Contact: Dr Marcella Bona
Prerequisite: Before taking this module you must take SPA4122
In this module, you will develop a broad range of skills in the practical analysis of real-world data. This will cover all of the major steps of data analysis, including the cleaning and pre-processing of datasets, initial analysis and visualisation techniques, the selection of appropriate methods to perform in-depth analyses and make statistical inferences from them, the fitting of meaningful physical models in the presence of imperfections and noise in the data, and the estimation of uncertainties and how they affect the conclusions that can be drawn. This module has an emphasis on the hands-on application of data analysis techniques using the Python and R programming languages, and is taught partly through lectures and partly through computer-based lab projects.
Connected course(s): UDF DATA
Assessment: 100.0% Coursework
Level: 5
