Module Details
| Module Code: |
COMP8054 |
| Title: |
Interactive Data Visualisation
|
|
Long Title:
|
Interactive Data Visualisation
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| NFQ Level: |
Advanced |
| Valid From: |
Semester 2 - 2019/20 ( January 2020 ) |
| Field of Study: |
4811 - Computer Science
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| Module Description: |
Web based data visualisation technology is a critical component of modern web applications. In this module the student will learn how to identify and apply a suitable visualisation technique for a data source. It will enable students to create visualisations of data for the web as well as the ability to incorporate interactive functionality to enhance data analysis.
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| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Discuss data visualisation techniques and principles. |
| LO2 |
Appraise the suitability of a variety of visualisation techniques for the web. |
| LO3 |
Apply a data visualisation technique to a data source. |
| LO4 |
Evaluate the suitability of interactive functionality incorporated into a visualisation technique. |
| LO5 |
Combine suitable data visualisation techniques and data sources for web-based viewing. |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
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| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
|
| No requirements listed |
| Indicative Content |
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Introduction
Data Visualisation Theory: Targeting appropriate visual elements on a page, mapping values in the data domain to visual domain, human visualisation interaction - adding computation steering to visualisations.
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Visualisation Techniques
Charts, Plots & Layouts - graphical representations of data: Line Charts, Area Charts, Bubble Charts, Bar Charts, Scatterplots, Scaling Data, Axes, Geomapping (GeoJSON, Paths, Projections). Layouts: Pie Layout, Stack Layout, Force Layout.
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Data Interactivity
Updating data, Interaction via Event Listeners, Transitions,Updating scales, Updating axes, Binding Event listeners, Grouping SVG Elements, Mouse Events, Multi touch devices, Zoom and pan behaviour, Drag behaviour.
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Data practices
Generating page elements, chaining methods, representing data in programming constructs, binding data, drawing with data, transition and animation support.
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Technologies
Web Standards, Canvas, Rendering the Box Model, CSS, Javascript, SVG, D3.js, JSON.
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Module Content & Assessment
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| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| No End of Module Formal Examination |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
The University reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Lecture |
Contact |
Theoretical content based on indicative content. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Practical implementation based on indicative content. |
Every Week |
2.00 |
2 |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Self directed study related to theory and implementation of assessed practical work. |
Every Week |
3.00 |
3 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
| Independent & Directed Learning (Non-contact) |
Non Contact |
Self directed study related to theory and implementation of assessed practical work. |
Every Week |
3.00 |
3 |
| Lecture |
Contact |
Theoretical content based on indicative content. |
Every Week |
2.00 |
2 |
| Lab |
Contact |
Practical implementation based on indicative content. |
Every Week |
2.00 |
2 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
|
Module Resources
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| Recommended Book Resources |
|---|
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Scott Murray. (2013), Interactive Data Visualization for the Web, Packt Publishing, [ISBN: 9781449339739].
| | Supplementary Book Resources |
|---|
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Nick Qi Zhu. (2013), Data Visualization with D3.js Cookbook, Packt Publishing, [ISBN: 9781782162162].
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Ben Fry. (2007), Exploring and Explaining Data with the Processing Environment, O'Reilly Media, [ISBN: 9780596514556].
| | This module does not have any article/paper resources |
|---|
| Other Resources |
|---|
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Website, The Visual Display of Quantitative
Information,
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