Scientific Visualization and Computer Graphics > CS > BI > FSE > RUG

Slides

This page lists the slides of the course. These are organized in modules, following a bottom-up structure (starting with simpler topics and ending up by putting it all together to design complete end-to-end applications).

Note 1: A module is not one-to-one to a class session.

Note 2: Depending on the actual pace (as a function of the level of the students in the class), some parts of certain modules may be skipped in the actual lecture. Make sure you are present at all lectures to follow the actually discussed material.

O. Course Set-up

This mini-module presents a quick overview of the aims and scope of the course, prerequisites, assignments structure, and way of grading.

0. Course setup

1. Introduction

We introduce Visual Analytics and relate it to other sub-disciplines of data visualization, such as scientific visualization and infographics. The specifics and challenges of Visual Analytics, especially in the context of exploring big data collections, are outlined.

1. Introduction

2. Data representation

Following the above-mentioned parallel between Visual Analytics and other types of visualization, we describe the types of data used in Visual Analytics. Concepts such as attribute types, (non)spatiality, hybridity, dimensionality, and multiscale representation are explained.

2. Data representation

3. Tableau basics

We introduce Tableau, a state-of-the-art framework for Visual Analytics, by means of a simple end-to-end and hands-on example. After this module, students should understand how to install Tableau, load a simple tabular dataset, and create some basic visualizations.

3. Tableau basics

4. Perception

The basic tenets of visual perception (of color, shape, orientation, animation, juxtaposition, texture, and Gestalt) are presented. Perception limitations are outlined by examples of optical illusions.

4. Visual perception

5. Visual encoding

Based on the perception principles outlined above, we introduce the visual encoding of data attributes into visual variables (e.g. color, shape, brightness, position, orientation). Pro's and con's of these visual variables are discussed, as well as good design guidelines.

5. Visual encoding

6. Tableau advanced

We continue the tutorial on Tableau by introducing several more advanced functionalities and chart types.

6. Tableau advanced

7. Visual analytics techniques

We put together the information on data types and visual encoding of data by presenting the main techniques used in information visualization. Techniques are discussed grouped by data type (individual attributes: color mapping; 1D and 2D signals: charts and timelines; data tables: table lenses, parallel coordinates, scatterplots, and projections; relational data: node-link layouts, treemaps, compound graph drawings, and edge bundling).

7. Visual analytics techniques

8. Application design

We detail the process of design of a visual analytics application: characterization of the problem domain, finding the questions to be answered, translating these to data types and visual techniques, and ending up by evaluation.

8. Application design

9. Case study

We end the course by presenting a case study of an end-to-end visual analytics application that uses several of the presented techniques in a concerted way to solve a real-world industrial problem involving big data.

9. End-to-end case study