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Data Science


  • Credit value: 30 credits at Level 6
  • Convenor: Dr Anthony Brooms
  • Assessment: two untimed quizzes (10% each), a timed test (30%) and a two-hour examination (50%)

Module description

In this module you will develop your understanding of statistical modelling techniques for data analysis. You will gain experience of using statistical languages/packages to carry out contemporary techniques for data analysis and forecasting that have wide-ranging applications.

Indicative syllabus

  • Exploratory data analysis for numeric and categorical variables
  • Statistical models and study design
  • Randomization methods for hypothesis testing
  • Bootstrapping methods for parameter estimation
  • Supervised learning (in particular classification, and regression, trees)
  • Unsupervised learning (in particular K-means, and hierarchical, clustering)

Learning objectives

By the end of this module, you will be able to:

  • understand and apply statistical techniques
  • apply tools of data analysis to a given set of data
  • make use of suitable statistical languages/packages to analyse data
  • transfer knowledge and expertise from one context to another, by applying statistical techniques in unfamiliar situations.