MSc Statistics with Data Science

Structure and course options for the Statistics with Data Science MSc programme.

You will take 120 credits of courses in total during Semesters 1 and 2, followed by a 60 credit dissertation which  you complete over the summer. The courses you take will be dependent on the availability of courses each year which may be subject to change as the curriculum develops to reflect a modern degree programme. 

 

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. 


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