Data Science
Overview
- Credit value: 30 credits at Level 6
- Convenor: Maura Paterson
- Assessment: coursework (20%) and a written examination (80%)
Module description
This module will allow you to develop a practical 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 module syllabus
- Importance of common distributions in modelling
- Stochastic processes in discrete and continuous time
- Monte Carlo simulation
- Fitting continuous models to data
- Multivariate and mixture distributions
- Supervised and unsupervised learning
- Predictive modelling
- Neural networks
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.