CS3DVNU: Data Integration and Information Visualisation

Module code: CS3DVNU

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

Credits: 20

Level: Level 3 (Honours)

When you'll be taught: Semester 1

Module convenor: Dr Nisha Singh, email: n.singh@reading.ac.uk

NUIST module lead: Xiaochen Lai, email: 003355@nuist.edu.cn

Pre-requisite module(s):

Co-requisite module(s):

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

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 27 June 2024

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 enhancing learning.  

Study hours

At least 44 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

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

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Each topic in a week has defined learning tasks which will enable students to self-reflect on the learning. Each practical session in a week will be severed as to facilitate the learning with personalised feedback provided towards the overall learning in this subject. 

Reassessment

Additional costs