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%)

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)

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.