Course Details in 2024/25 Session

Module Title	LI Foundations and Applications of Data Science
School	Physics and Astronomy
Department	Physics & Astronomy
Module Code	03 37965
Module Lead	William Chaplin
Level	Intermediate Level
Credits	10
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-24 hours Guided independent study-76 hours Total: 100 hours
Exclusions
Description	The module will cover key mathematical foundational concepts and methods relevant to Data Science, including: Sampling theory (Nyquist-Shannon)Non-square matrices; singular value decomposition (SVD); matrix norms; dualityMultivariate statistics: Introduction, Bayes theorem, priors, posteriors; hypothesis testing; frequentist versus BayesianCramer Rao criterion; distribution of p values; sampling distributions; non-parametric testing (e.g. odds ratio); advanced statistical methodsImplications of the Central Limit theorem Sampling algorithms (e. g. Metropolis Hastings)
Learning Outcomes	By the end of the module students should be able to: Apply mathematical descriptions of the information content of data, including Nyquist-Shannon sampling theory Calculate small SVDs by hand and understand rank deficiency Apply Bayes theorem in different contexts, e.g. , to multivariate parameter estimation problems Derive and apply suitable statistical tests Interpret the implications of the central limit theorem Be able to implement a sampling algorithm, and be cognisant of different techniques
Assessment
Assessment Methods & Exceptions	Assessment: One 1.5 hour exam (80%) and continuous assessment (20%) [NOTE: under EPS exception to CoP] Reassessment: Exam in resit period
Other
Reading List