Statistical Inference Specialism

The statistical inference specialism gives students the ability to derive reliable insight from the most complex data

The Statistical Inference specialism of the Data Science MSc is aimed at students with a background in mathematics and statistics and who want to develop their statistical, computing and analytical skills for the extraction, synthesis, processing and analysis of large and complex data.

In addition to the core modules of Data Science Fundamentals, Programming for Data Scientists and Data Mining, students will also take Bayesian Inference for Data Science and 2 further compulsory modules in statistical methods. It is anticipated that most students on this pathway will take the advanced modules in Generalised Linear Modelling and Likelihood Inference rather than the more general modules in Statistical Methods and Modelling and Statistical Inference.

Students will be able to shape their learning according to their interests by selecting from the following optional modules:

Three from:

  • Forecasting
  • Data mining for marketing, sales and finance
  • Optimisation and heuristics
  • Principles of epidemiology
  • Environmental epidemiology
  • Extreme value theory
  • Genomics: technologies and data analysis
  • Clinical trials
  • Survival analysis
  • Longitudinal data analysis

Within this specialism, students interested in a career in Health Data Science or Business Analytics are able to further specialise by selecting the modules of the Health Pathway or the Business Intelligence Pathway.

The Statistical Inference specialism encompasses data science fundamentals but with additional focus upon statistical modelling and inference of large and complex data structures. More specifically, the course provides a thorough ‘core’ training in statistical theory; data analysis and computing via a distinctive blend of leading-edge methodology and practical techniques (including Bayesian and computational methods and data mining) and ‘optional’ modules spanning, for example, genomics, longitudinal data analyses, time-to-event data, spatial data analyses and forecasting. The modules reflect inter-departmental research expertise and will prepare students for particular career options in areas with growing demand for data scientists.

Please contact Dr Deborah Costain for further information.