Registry

Module Specifications

Archived Version 2021 - 2022

Module Title
Module Code
School
Online Module Resources
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This module aims to develop an understanding of the management and structuring of large datasets. This module will develop an understanding of the critical role of exploratory data analytics, data quality and data governance within a data analytics pipeline. Techniques for data visualization, particularly of large datasets, will be discussed and implemented.

Learning Outcomes

1. Analyse the requirements of applications handling large datasets.
2. Demonstrate an ability to efficiently process a large dataset.
3. Practice data quality and data cleaning measures.
4. Critique data-driven visualisations based on their communication goals and effective use of visualisation methods and techniques.
5. Create effective data-driven visualisations.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Independent Study	187.5	No Description
Total Workload: 187.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Data Collection
Volume, Velocity, Variety and Veracity; Parsing structures and unstructured data

Data Management
Understanding requirements for data management; queries and impact on data management; an data-driven information lifecycle; mapping, transformation and pre-processing, data annotations and metadata.

Data Quality
Data Cleaning; Accuracy; Completeness; Relevance; Consistency across data sources; Reliability; Accessibility.

Data Visualisation
What is data visualisation? Visualisation basics; Traditional forms of data visualisation; Visualising multi-dimensional data; Visualising large datasets (geo-spatial data, temporal data); Interactive visualisation. Use of a data visualisation tool or platform to create data driven visualisations.

Evaluation of Visualisation
Understanding of effective communication and best practice for creating data visualisations.

Assessment Breakdown
Continuous Assessment	%	Examination Weight	%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
Unavailable

Indicative Reading List

Andy Kirk: 2016, Data Visualisation: A Handbook for Data Driven Design, 9781473912144
Cole Nussbaumer Knaflic: 2015, Storytelling with Data: A Data Visualization Guide for Business Professionals, 1119002257
Field Cady: 2017, The Data Science Handbook, 9781119092919

Other Resources

None

Programme or List of Programmes

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