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The student will familiarise with the different types of relational data and the related statistical models. The student will be able to manipulate data stored as a network, and to choose and implement appropriate statistical methodologies to analyse these data.
Indicative Module Content:Topological properties of networks. Erdos-Renyi random graph. Community detection. Stochastic Block Models. Latent Position Models. Exponential Random Graph Models. Network Autocorrelation Models.
The module has a strong focus on programming with R, and on the computational aspects of the statistical methodologies that are introduced.
| Student Effort Type | Hours |
|---|---|
| Specified Learning Activities | 36 |
| Autonomous Student Learning | 60 |
| Online Learning | 24 |
| Total | 120 |
Background on statistical inference including probability spaces, likelihood-based inference, regression is essential. Students should be familiar with linear algebra and calculus.
Learning Recommendations:Familiarity with R, or with a computer programming language that is related to data science.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Continuous Assessment: Homework assignments and online quizzes. | Varies over the Trimester | n/a | Other | No | 40 |
| Project: Final project. | Varies over the Trimester | n/a | Other | No | 60 |
| Resit In | Terminal Exam |
|---|---|
| Autumn | No |
• Group/class feedback, post-assessment
Not yet recorded.