F29VA - Visual Analytics

Course leader(s):

Aims

To provide students with the theory, principles, and tools to enable them

To understand the fundamental concepts and techniques used in the essential visual analytics process
To apply analytical expressions to conduct time-based analyses, and visualize data effectively, and identify patterns ,and make data-driven predictions.
To convey ideas effectively, considering both aesthetic form and required functionality that will provide insights into different types of dataset (Multi-variate data, temporal data, spatial and geospatial data, graphs, trees, and other network-type data, text, document)
To implement interactive visualization reports and dashboards for comprehensive data presentation.

Syllabus

1. Introduction to Visual Analytics

2. Data Foundations

3. Exploratory Data Analysis

4. Data Visualization and Design

5. Human Perception and Processing of Information ( Interaction Concepts)

6. Visualization Techniques for Different Data Types - Multi-variate data - Temporal data - Spatial and Geospatial data - Graphs, trees, and other network type data - Text, document, other multimedia data visualisation

7. Comparing and Evaluating Visualization Techniques

Learning outcomes

By the end of the course, students should be able to do the following:

utilize appropriate data visualization tools and data analysis methodologies to create various infographics, interactive elements, and design options.

conduct quantitative and qualitative research on real-life, complex data sets by designing own research questions and apply suitable visualisation tools and method.

demonstrate the capacity for critical analysis and solution selection, deal with complex issues and make informed judgements by using appropriate computer software to process data, and to support and enhance the research tasks.

demonstrate the ability to learn independently and demonstrate leadership/initiative in tackling research problems.

use a wide range of resources to present results in a way that demonstrates a good understanding of the technical and broader issues of data visualization.