F21DV - Data Visualisation and Analytics

Course leader(s):

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

To create engaging and intuitive graphical and interactive applications that allow users to search, explore, reveal, partition, understand, discover and communicate the structure and information in large data sets;
To convey ideas effectively, considering both aesthetic form and required functionality that will provide insights into different types of dataset (big data, complex data, heterogeneous data, linked data, dynamic data, dirty data);
To stimulate user engagement, attention and discovery;
To elicit main requirements of such systems;
To be able to implement interactive web-based visualisation systems and assess their effectiveness

Syllabus

1. Analytics (1.1 Data Analytics, 1.2 Networks, 1.3 Beyond Analytics)

2. Visualisation (2.1 Introduction to Data Visualisation, 2.2 Visualisation Components, 2.3 Storytelling and Perception, 2.4 Space and Time)

3. Design & Interaction (3.1 Interaction, 3.2 Dashboards, 3.3 Design and Evaluation)

Learning outcomes

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

Demonstrate a detailed understanding of data analysis and data visualisation processes

Demonstrate extensive knowledge of visualisation and interaction paradigms

Apply appropriate analysis and visualisation techniques to investigate datasets with different data types and complex data structures

Compose visual mapping and interaction techniques to design and implement complex and interactive data visualisation systems

Critically reflect on the perception of data visualisations by humans and the implication for design choices

Critically evaluate the appropriateness and effectiveness of data visualisation and analysis techniques for an identified audience

Communicate and work in collaboration with peers or specialists

Further details