Course description

Visualisations and Network Theory

Introduction

Digital technologies are increasingly permeating the way we work, live, and think. A crucial aspect of the digital transformation lies in the ever increasing amount of data that is being produced about individuals and organizations alike. Big data is the keyword to characterize the unprecedented volume, velocity, and variety of data being produced in the digital age. Increasingly, organizations are harnessing the power of big data through data mining and analytics. Gaining insights from big data can be challenging and requires specialized knowledge, also in data visualization. This course thus intends to equip students with a set of analysis techniques to make sense of data in a visual way. A focus will be on social network analysis to study relational data, identify influential nodes in a network, and distinguish communities. Moreover, students will learn to collect trace data in order to visualize it through widely used software. In addition to network data, the data being analyzed includes unstructured textual data, temporal data, geospatial data, or a combination of these.

The general objective of the course is to provide students with a solid grasp of the tools and techniques of information visualization, and to help them design insightful information visualizations. The emphasis will be on how to convey insights to interested audiences, and we will discuss the challenges of finding a fit between audience needs and the right data presentations. Both structuration principles for arguments, as well as data presentation tools, including reports, dashboards, visualizations, and key metrics will be explained. Using approaches from information visualization research, internet geography and social network analysis, techniques to gain insights on the "when" (temporal data), "where" (geospatial data), "what" (topical data), and "with whom" (networks and trees) from data will be explored.

Course content

Data Visualization
An overview of data visualization techniques is given, showcasing current examples in the form of interesting research and company studies. Students will get a first grasp of practical questions and challenges related to visualizations that come with large amounts of often unstructured data. In addition, students will be provided with a course overview and detailed description of the assignment.  

Essentials of Data Collection
Sources for gathering data, from APIs to repositories to services will be introduced. The students will explore different software tools and learn how collect social media data.

Descriptive Data Visualization of Numerical Data
Based on data collected and sample data sets, students will learn how to visualize basic distributions and tendencies in data through scatterplots, bar charts, histograms and similar visualization techniques.

Visualization of Textual Data
Students will be exposed to methods for extracting information and insights from large volumes of unstructured text through text mining and subsequent visualization, using word clouds and sentiment analysis.

Principles of Social Network Analysis I
The scientific origins of social network analysis will be introduced, including some fundamental concepts from graph theory. Furthermore, students will be familiarized with concepts such as ego-, group-, and global networks, and their applicability to real-world challenges.

Principles of Social Network Analysis II
Core concepts of social network analysis will be introduced further, such as network structure, and network centrality.

Practice-Oriented Social Network Analysis
Students will learn to use software for social network analysis, such as Gephi and Netlytic, through hands-on tutorials and exercises.

Geospatial Data Visualization
Geographic information visualization through maps and infographics will be conveyed.

Ethical Considerations of Working with Data Visualizations
Privacy considerations and implications as well as legal questions (copyright, licensing, publication and data sharing) are discussed.

Learning outcome knowledge

After taking this course, students should have acquired knowledge of:

  • temporal, geospatial, topical, and network data
  • the infrastructure for visual and network data analytics

Exam organisation

  • Presentation and discussion: 30%
  • Written assignment: 70%