CS3DV: Data Integration and Information Visualisation

Module code: CS3DV

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: 6

When you’ll be taught: Semester 1

Module convenor: Dr Nachiketa Chakraborty , email: n.chakraborty@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 3 April 2025

Overview

Module aims and purpose

This module introduces students to the concepts, principles, design methodologies, and tools of data integration and extracts information to transform the raw data into visual insights. These insights can then be effectively utilised to support decision-making processes and build knowledge. Students will develop an understanding of data integration employed in pre-processing multidimensional, multimodal data from homogenous and/or heterogeneous sources and formalising datasets for analytics in solving computing problems with knowledge and wisdom. Students will also study various data visualisation methodologies and tools adapted to implement interactive dashboards showing 360o contextual views required by a given scenario.

Students will also be able to demonstrate their abilities in:

• team-working and communication;
• critical reflection towards quality and impact of design process and outcomes;
• effectively use of commercial software tools (e.g., Tableau); and
• professional and effective writing for software design documents.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Establish a sound understanding of the essential concepts and principles of data integration and information visualisation;
2. Develop data-driven approaches for information discovery and processing in a domain context through using methods and techniques to pre-processing data and analysing data toward insights;
3. Design and implement features of data integration and visualisation which can perform a set of functions, such as ETL/ELT, multimodal, multidimensional datasets, data warehouse, and interactive dashboards; and
4. Incorporate social and ethical aspects in gathering data, processing data and visualising information.

Module content

The module covers the following topics:

• Introduction of concepts of data fusion and data fusion process of integrating homogenous and heterogenous data in multiple data sources
• Data integration methods and techniques, e.g., ETL/ELT, for processing data/big data
• Data architecture and design, e.g., star schemas, temporal dimensions, and cubes, for producing meaningful insights
• Information visualisation methods and techniques, e.g., distribution correlation, charts, story points and interactive dashboards, applied in given problem contexts
• Social and ethical implications in designs of data integration and information visualisation

Structure

Teaching and learning methods

This module will take a problem-based learning approach. The lectures will introduce students the theories, concepts and underpinning principles specified in the module content. Students will be supervised in the practical sessions to apply the concepts and design principles to a given problem context and develop a technical solution.

There will also be learning materials in digital forms when they are required to support learning.

There are two types of assessment (i.e. formative assessment and summative assessment) which will support and reinforce students’ learning. A formative assessment is carried out through weekly learning activities. Summative assessment consists of written coursework assignment and written examination. Appropriate feedback will be timely communicated with students for