Network Analysis and Infographics (959N1)
15 credits, Level 7 (Masters)
Our capabilities to collect and access data about phenomena (e.g. social media, geolocation data, biological interactions) have increased considerably over the last decade. But making sense of these vast stores of data remains a big challenge. As a result, data scientists have become increasingly important for both private- and public-sector organisations. And data scientist jobs are currently among the best-paying jobs.
Network analysis is an important methodology that enables us to examine and visualise small and large data and to make sense of these by revealing structures and patterns. This module introduces you to:
- qualitative and quantitative approaches to collect network data and analyse various network types
- basic principles for generating network data-based information graphics that are capable of conveying rich information with relatively simple infographics.
Lectures focus on introducing the concepts and methodological approaches of network analysis and infographics. Your seminars are computer-based sessions that introduce you to three main software packages (R and its igraph package, Gephi, and VOSviewer) to perform network analysis and produce infographics.
50%: Coursework (Group presentation, Group submission (written))
50%: Written assessment (Essay)
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.
This module is running in the academic year 2021/22. We also plan to offer it in future academic years. However, we are constantly looking to improve and enhance our courses. There may be changes to modules in response to student demand or feedback, changes to staff expertise or updates to our curriculum. We may also need to make changes in response to COVID-19. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.