Network Analysis ECON5152

  • Academic Session: 2024-25
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No

Short Description

Networks arise in most fields of study, as they are a simple yet powerful tool to describe relationships between units (individuals, firms, countries, genes, species, etc.). This course introduces networks and the mathematical background used to understand them; describe how networks shape many aspects of our interconnected world; and provide students with hands-on experience analysing network data.

Timetable

One 2-hour lecture per week for 10 weeks

Five 2-hour computer labs.

On campus.

The course employs a flipped approach to case teaching and programming tasks. Students will spend approximately 15 hours working asynchronously on assigned materials.

Requirements of Entry

Students must be registered on one of the associated programmes listed in this course specification.

Excluded Courses

None.

Co-requisites

None.

Assessment

ILO(s)

Course Aims

■ Provide students with the tools to describe and visualize networks, to understand their ubiquity across many fields, and to use theoretical or statistical models to learn from network data.

■ Emphasis is placed on providing tools that can be used for understanding networks regardless of the context in which they arise. The lectures focus on introducing the concepts and mathematical language (graph theory) that is required to understand networks.

■ As data on networks is becoming increasingly widespread, the lab workshop component will provide the required programming knowledge for network analysis and an opportunity to apply the concepts to real-world data.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Classify how and why networks arise naturally in many social and biological processes.

2. Employ theoretical models to study different aspects of networks and their properties.

3. Apply statistical models to discover patterns in network data and interpret the results from real-world applications of network analysis.

4. Visualize, describe and model network data through the use of programming tools.

5. Collaborate effectively within a group work environment to deliver a final project and develop teamworking skills.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.