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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 |
---|---|
Lectures | 24 |
Computer Aided Lab | 12 |
Specified Learning Activities | 24 |
Autonomous Student Learning | 60 |
Total | 120 |
Background on statistical inference including probability spaces, likelihood-based inference, regression is essential. Students should be familiar with linear algebra and calculus.
Familiarity with R, or with a computer programming language that is related to data science.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Project: Final project | Varies over the Trimester | n/a | Other | No | 60 |
Continuous Assessment: Continuous assessment | Varies over the Trimester | n/a | Other | No | 40 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Group/class feedback, post-assessment
Not yet recorded.