Data Visualisation (Level 8)

Duration i year

Stage Award

NFQ Level Level 8

ECTS Credit 10

The module aims to facilitate learners in engaging with the concept of data visualisation strategy, its formulation and implementation.

The objective of this module is to provide the learner with knowledge of:

The theoretical and practical aspects of Data Visualisation datasets.
An overview of the initial collection of data to be visualised.
A formal grounding in visualisation approaches.
Apply principles of statistical analytics to visual data.
Developing knowledge and understanding of tools that can be used for visualisation.
Applying these techniques and principles in typical real-world scenarios.

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Indicative Syllabus

Theory and Concepts of Data Visualisation

History of data visualisation.
Understand the various categories used in the field e.g. Information/data/scientific visualisation, infographics, visual analytics.
Investigate theorists and best practice in these fields, e.g. cognitive amplification, perceptual enhancement and ways to encourage inferential processes.

Data visualisation pre-processing techniques

Learn data cleaning techniques relevant to data visualisation – data aggregation, data sampling, impute missing data, find inconsistencies.
Learn transformation techniques – data normalisation, construct new variables, Investigate how to use regular expressions and data manipulation techniques to pre-process data sets.

Data Visualisation traditional statistical approaches

Histograms, boxplots, scatter plots.
Analysing correlations and patterns between variables.
Univariate, bivariate and multivariate ways of presenting data.

Advanced visualisation techniques

Investigate computer based tools for visualisation and their features – interactivity, geospatial methods, hierarchical and networks solutions.

Visual Analytics

Understand and critique the various visualisation methods used to solve data mining and data analytics problems, e.g. anomaly detection, pattern discovery.

Data Analytics Techniques

Investigate the main pitfalls in data visualisation and data analytics in a real-world setting.
Compare and contrast data analytics techniques investigating their theoretical principles, assumptions, strengths and weaknesses.

Module Details

Minimum Intended Learning Outcomes (MIMLOS)

Describe the concepts, principles and methods of data visualisation.
Select and apply a variety of data explorative and pre-processing techniques to a range of data visualisation problems.
Design and implement appropriate data visualisation techniques to solve data analytical problems.
Interpret, critique and communicate patterns and knowledge discovered as a result of applying data visualisation techniques to a variety of data sets and analytical problems.
Research and appraise a variety of data analytics solutions to current challenges in the area.

Assessments / Exams

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board Meeting and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Data Visualisation (Level 8)

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