CS3DV20NU-Data Integration and Visualisation

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:6
Semesters in which taught: Semester 1 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Lily Sun
Email: lily.sun@reading.ac.uk

NUIST Module Lead: Xiaohe Zhang
Email: xiaohe.zhang@nuist.edu.cn

Type of module:

Summary module description:

This module focuses on key aspects of data integration and data visualisation, covering concepts, principles, techniques and tools for the effective analysis of data. Students will learn techniques for processing various types of data for information visualisation. The students will be encouraged to test their technical abilities for data integration and develop their creative skills in visualising data to support data-driven decision making.

Aims:

This module aims to introduce the concepts, principles, design methodologies, and tools of data integration and data visualisation with the objective of transforming the raw data into insights that can effectively support decision-making processes. Students will develop the knowledge of data integration that is employed in processing data of multiple types and from multiple sources. Students will also study various data visualisation methodologies and tools which are adopted to implement interactive dashboards showing 360o contextual views. This module will enable students to attain skills in the effectiveness of data integration and knowledge representation.

This module will aim to develop the following graduate attributes, such as problem solving, creativity, team working, and effective use of commercial software.

Assessable learning outcomes:

On successful completion of the module, students will be able to:

• Critically choose and then apply appropriate methods to conduct data integration and data visualisation;


• Have a sound understanding of the essential concepts and principles of data integration and data visualisation techniques;


• Develop data-driven approaches for information discovery and processing in a domain context through data integration and data visualisation;


• Design and implement a data integration and visualisation tool which can perform a set of functions, such as ETL, multidimensional datasets, data warehouse, and interactive dashboards;


• Be aware of trends of data integration and data visualisation in relation to data analysis and its value to people’s work and life.


• Be aware of trends of data integration and data visualisation in relation to data analysis and its value to people’s work and life.

Additional outcomes:

Outline content:

• Context: Importance of data visualisation and its historical account.


• Nature of data and data sources diversity


• Data integration methods and technologies, e.g. ETL (extraction, transformation and load)


• Data warehousing strategy, architecture and design (star schemas, temporal dimensions, cubes, etc.)


• Critical analysis using multidimensional datasets


• Types of data visualisation methods (e.g., distribution correlation, ranking) and charts


• Data visualization design techniques and effective presentation (e.g., understanding data statistics)


• Interactive Dashboards


• Impact of designs on the presented statistics Type of tools (e.g., Tableau)


• Real-world application domains and requirements (e.g., financial trends, genetics, regression)

Brief description of teaching and learning methods:

The module is delivered by lectures and Lab practicals.