Module Code: |
H6DV |
Long Title
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Data Visualisation
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Title
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Data Visualisation
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Module Level: |
LEVEL 6 |
EQF Level: |
5 |
EHEA Level: |
Short Cycle |
Module Coordinator: |
Adriana Chis |
Module Author: |
Adriana Chis |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
Master’s degree and/or PhD degree in computing or cognate discipline. May have industry experience also
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Analyse the theory and concepts relating to visualisation and data representation |
LO2 |
Evaluate and distinguish between visualisation techniques for specific problems to enable effective communication of data analysis |
LO3 |
Design, develop, and implement processes for data visualisation |
LO4 |
Propose a suitable visualisation design for a particular combination of data characteristics and application |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI 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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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
Learners should have attained the knowledge, skills and competence gained from stage 1 of the BSc (Hons) in Data Science
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Module Content & Assessment
Indicative Content |
Introduction
• What is Data Visualisation?
• Characteristics of Data, Data Types and Information, Ethical issues with sourcing datasets
• Communication through visualisation
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Visualisation Design
• Data Visualisation Techniques
• Principles and Workflow of data visualisation
• Graphical integrity
• Clarity of data representation
• Elements of visual design (layout, colour, fonts, labelling, annotation, etc.)
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Data Visualisations
• Vector fields and flow data
• Time-varying data
• High-dimensional data: dimension reduction, parallel coordinates
• Non-spatial data: multi-variate, tree/graph structured
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Evaluation of Visualisation Methods
• Small and large data sets
• Suitable visualisation design
• Data and application characteristics
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Interactivity
• Data adjustments
• Presentation adjustments
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Applications of Visualisation
• Scientific, medical, mathematical data
• Spatial Analysis
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Continuous Assessment |
% of total: |
Non-Marked |
Assessment Date: |
n/a |
Outcome addressed: |
2,4 |
Non-Marked: |
Yes |
Assessment Description: Ongoing independent and group design and development of visualisations using different types of data, visualisations techniques and tools. Feedback will be provided throughout these activities. |
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Assessment Type: |
Continuous Assessment |
% of total: |
50 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
No |
Assessment Description: Learners are required to develop clear and effective visual representations of some of the features of a dataset(s). For example, learners will first create a number of visualizations, and then will create an infographic to highlight key information found in the dataset(s). The assignment includes a report to document the process for creating the visuals, to justify the techniques, layout, style, colours used. |
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Assessment Type: |
Project |
% of total: |
50 |
Assessment Date: |
n/a |
Outcome addressed: |
2,3,4 |
Non-Marked: |
No |
Assessment Description: Learning outcomes may be assessed through a project in which learners must choose and ethically acquire a set of raw data; design, develop, and document a process from preparing the data through to implementing interactive data visualisations or a number of static data visualisations; analyse the results; and provide an evaluation of the correct use of data and visual techniques that were implemented. |
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No End of Module Assessment |
Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
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Reassessment Description The repeat strategy for this module is a terminal assessment. Students will be afforded an opportunity to repeat the assessment at a specified time during the academic year and all learning outcomes will be assessed in the repeat assessment.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
Practical |
Other hours (Practical/Tutorial) |
12 |
Per Semester |
1.00 |
Independent Learning |
Independent learning (hours) |
89 |
Per Semester |
7.42 |
Total Weekly Contact Hours |
3.00 |
Module Resources
Recommended Book Resources |
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Kirk, A. (2016). Data Visualisation. Sage Publishing..
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Ward, M., Grinstein, G. & Keim, D. (2010). Interactive Data Visualization: Foundations, Techniques, and Applications. A. K Peters Ltd..
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Ware, C. (2012). Information Visualization: Perception for Design. (3rd ed.). Morgan Kaufmann..
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Barker, T. (2013). Pro Data Visualization using R and JavaScript. Apress..
| Supplementary Book Resources |
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Tufte, E.R. (2001). The visual display of quantitative information, Graphics Press Cheshire. Conn..
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Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders..
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Chang, W. (2013). R Graphics Cookbook. O'Reilly Media..
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Murrell, P. (2011). R Graphics. (2nd ed.). CRC Press..
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Janert, P.K. (2010). Data Analysis with Open Source Tools. O'Reilly Media..
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Steele, J. & Iliinsky, N. (2011). Designing Data Visualizations. O'Reilly Media..
| This module does not have any article/paper resources |
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This module does not have any other resources |
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