CS3DV20-Data Integration and Visualisation

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Mr Miguel Sanchez Razo

Email: m.sanchezrazo@reading.ac.uk

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 int egration 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. 

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., distri bution 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.

Contact hours:

	Autumn	Spring	Summer
Lectures		10
Practicals classes and workshops		10
Guided independent study:
Wider reading (independent)		20
Exam revision/preparation		20
Peer assisted learning		10
Preparation for tutorials		10
Preparation of practical report		20
Total hours by term	0		0
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	30
Set exercise	70

Summative assessment- Examinations:

One 1.5-hour examination paper in May/June.

Summative assessment- Coursework and in-class tests:

One problem-based assignment.

Formative assessment methods:

The weekly guided practical sessions will support students to enhance their understanding and receive feedback. The weekly outcomes will be consolidated and refined towards the written assignment.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

One 2-hour examination paper in August/September.  Note that the resit module mark will be the higher of (a) the mark from this resit exam and (b) an average of this resit exam mark and previous coursework marks, weighted as per the first attempt (30% exam, 70% coursework). 

Additional Costs (specified where applicable):

Last updated: 10 November 2020