GRA 4143 Visualisations and Network Theory
GRA 4143 Visualisations and Network Theory
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, topcial 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 research and social network analysis, techniques to gain insights on the "what" (topical data), "with whom" (networks and trees), and "when" (temporal data) from data will be explored.
After taking this course, students should have acquired knowledge of:
- network, topical and temporal data
- the infrastructure for visual and network data analytics
After taking this course, students should be able to:
- apply widely used software and analytics packages for visualization purposes
- conduct social network analyses based on social media and trace data
- combine different visualization and analysis methods to design network diagrams, conceptual maps, and meaningful visualizations.
After taking this course, students should:
- have developed a holistic understanding for the role of the visual for business analytics and scientific research
- be up-to-date about current debates in this area
- be able to critically assess different data visualization approaches in terms of their strengths and weaknesses
- be able to reflect on the limitations of big data-based visual and statistical methods
- be aware of possible ethical implications of dealing with sometimes sensitive data, for example in terms of consent, fair use and general data protection
- be able to interpret data in a broader context
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.
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.
Ethical Considerations of Working with Data Visualizations
Privacy considerations and implications as well as legal questions (copyright, licensing, publication and data sharing) are discussed.
The course aims at combining formal lectures with group and individual exercies. The course will consist of the following elements:
- Formal lectures for basics of the topics and to provide a conceptual framework;
- Case studies and tutorials for deepening knowledge of the research process, as well as for applying theoretical knowledge to real-world situations;
- Potentially guest lectures by practice experts in order to gain insights on data visualization.
Computer-based tools
Gephi, Tableau, Netlytic, Sentistrength, R
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam components will get a lower grade or may fail the course. Information about the point system and the cut off points with reference to the letter grades, will be given when the course starts.
All components must, as a main rule, be retaken during next scheduled course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Activity Form of assessment: Presentation and discussion Weight: 40 Grouping: Group (2 - 3) Duration: 20 Minute(s) Comment: Students are expected to produce a poster on a topic of their choice, based on insights gained through social network analysis or other visualization techniques conveyed in the course. To create this poster, students will gather primary data on their chosen topic, relying on the approaches outlined in class. The students will present the poster in the final class session. Exam code: GRA 41431 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Group (2 - 3) Duration: 6 Week(s) Comment: Students are expected to document their data gathering and analysis approach in a paper that expands on and contextualizes their poster (no longer than 15 pages). Exam code: GRA 41431 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 30 Hour(s) | |
Prepare for teaching | 40 Hour(s) | |
Student's own work with learning resources | 40 Hour(s) | |
Group work / Assignments | 50 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.