Data visualization

Course goals

After completing the course the student will:

• have an overview of the state-of-the art visualization methods for relational and high-dimensional data (Know);
• be able to explain how these methods construct visualizations (Understand)
• select an appropriate data visualization method and parametrize it when presented with high-dimensional or relational data (Apply)
• assess the results of the constructed visualizations (Evaluate)
• present and motivate all taken choices (Defend)

Assessment
The grade for the course is computed as follows:

• process (consistency, quality, and completeness of intermediate presentations)(25% of the final mark)
• final project presentation (25%)
• final project report (50%)

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Content

This course teaches Data Visualization methods with a focus on relational and high-dimensional data. 
Such data appear in many of real-life applications from science, social phenomena, and engineering.

The course is divided into two parts:

1. relational data
2. high-dimensional data

For relational data we present a model used for representation and storing relational data, called graph or network, and list applications of network visualization.

We define so-called quality metrics to construct and assess network visualizations. In the following we study several network visualization methods, including:
- hierarchy (tree) visualization
- visualizing general graphs
- visualization of multilevel networks for modeling highly complex applications
- graph bundling.

For high-dimensional data we present methods that teach the visualization of large data tables (thousands of rows, hundreds of columns) from data science, artificial intelligence, and related fields.

We discuss these methods as well as quality metrics to assess them including:
- parallel coordinate plots
- scatterplot matrices
- table lenses
- dimensionality reduction

Course form

• weekly lectures
• group project work
• intermediate update meetings.

The students

• are provided with datasets to visualize with the taught methods
• implement presented methods and explain their design decisions
• evaluate their visualizations with presented methods.