modules

In this course we look at the concepts and methods of modern statistics in greater detail. The course will cover various topics in statistical modelling with Bayesian flavor, including generalised linear models, Hierarchical statistical models, Generative and Discriminative models, Hidden Markov models, use of Markov Chain Monte Carlo and Gaussian processes. The module will include practical application of these techniques as well as theoretical underpinnings and model choice.

 

Pre-requisites: COMM415 DA Fundamentals of Data Science (Professional)

Co-requisites: None.

 

This module is a part of the dual-qualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes. After successful completion of the programme, students will graduate with MSc Data Science and (subject to additional completion of the End Point Assessment) the Level 7 Research Scientist Apprenticeship. 

 

The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.