MSc Statistics with Data Science

Your compulsory courses will build strong statistical foundations and associated programming skills. All courses are worth 10 credits, unless otherwise indicated.

Semester 1 compulsory courses have previously included:

• Bayesian Theory
• Extended Statistical Programming (20 credits)
• Generalised Regression Models ​​​​

Semester 2 compulsory courses have previously included:

• Bayesian Data Analysis
• Design and Sampling for Data Science
• Statistical Research Skills 

The optional courses cover a wide range of areas, including data analysis, machine learning and optimization. All courses are worth 10 credits, unless otherwise indicated.

Semester 1 optional courses have previously included:

• Applied Machine Learning (20 credits)*
• Fundamentals of Operational Research
• Fundamentals of Optimization
• Python Programming
• Statistical Methodology
• Stochastic Modelling
• Theory of Statistical Inference

Semester 2 optional courses have previously included:

• Biostatistics
• Credit Scoring
• Incomplete Data Analysis
• Large Scale Optimization for Data Science
• Machine Learning in Python
• Methods for Causal Inference*
• Multivariate Data Analysis
• Nonlinear Optimization
• Simulation
• Targeted Causal Learning
• Time Series 

Full year optional courses have previously included:

• Text Technologies for Data Science (20 credits)*

*delivered by the School of Informatics

Your  individual dissertation will usually take the form of two consultancy-style projects. Each consultancy-style project will last 5-6 weeks and will take the form of a consultant-client style where an industrial client will present a problem to your class and each student acts as a consultant addressing a particular aspect of the problem and presenting their conclusions to the client at the end of the 5-6 week period.