Network Analysis and Infographics (959N1)

15 credits, Level 7 (Masters)

Spring teaching

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.

Teaching and assessment

We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.

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 2020/21. We also plan to offer it in future academic years. It may become unavailable due to staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of such changes to modules at the earliest opportunity.


This module is offered on the following courses: