Module: Data Management and Visualisation

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Data Management and Visualisation
Module Code	CA682I
School	School of Computing
Module Co-ordinator	Semester 1: Suzanne Little Semester 2: Suzanne Little Autumn: Suzanne Little
Module Teachers	Suzanne Little
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat examination

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	25%	Examination Weight	75%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Process (explore, clean, format) two or more datasets to produce an analysis of their composition and create a data visualisation demonstrating communication skills.	25%	n/a

Reassessment Requirement Type
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
This module is category 3

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

The module is delivered using the FutureLearn platform and is divided into 5 themes, each hosted on a separate MOOC. It is the online version of CA682 delivered on the MCM programme.

Programme or List of Programmes

MCM	M.Sc. in Computing

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