STAT41010 Stat Network Analysis

Academic Year 2022/2023

The module focuses on the analysis of relational data, and reviews the available methodologies and algorithms that can be employed to model network interactions.

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Curricular information is subject to change

Learning Outcomes:

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 Hours: 
Student Effort Type Hours
Lectures

24

Computer Aided Lab

12

Specified Learning Activities

24

Autonomous Student Learning

60

Total

120

Approaches to Teaching and Learning:
Weekly lectures and computer labs. 
Requirements, Exclusions and Recommendations
Learning Requirements:

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.


Module Requisites and Incompatibles
Incompatibles:
STAT40960 - Stat Network Analysis (online)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % 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


Carry forward of passed components
No
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

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