Module: Data Analysis & Visualisation

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Analysis & Visualisation
Module Code	CA121 (ITS) / CSC1007 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Claudia Mazo
Module Teachers	Denise Freir, Graham Healy
NFQ level	6	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Repeat examination

Description

The module aims to equip students with an understanding of data analysis and visualisation techniques and the knowledge of a variety of tools and statistical techniques to make sense of the emergence and exponential growth of big data. It will teach students to identify suitable approaches for business related issues.

Learning Outcomes

1. Understand the data analysis pipeline, how it is used in organisations and the ethical implications.
2. Explain the purpose and benefits of data analysis, in particular the emergence of big data.
3. Understand the requirements for communicating data analysis through visualisations and critique both their own and other visualisations.
4. Use functions in spreadsheets such as import, filtering, manipulation, visualisation.
5. Identify and specify requirements (such as tools, data and organisational structure) for performing the analysis of complex business-related issues.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	Lectures and in-class tutorials covering key topics of the course.
Laboratory	12	Laboratory hands-on experience running appropriate software.
Independent Study	89	Preparation for and reading after lectures.
Total Workload: 125

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 analysis
big data, appropriate data selection processes, representative sampling, understanding requirements (data tools, organisation structures etc), using spreadsheets for analysis

Data visualisation
selecting appropriate graph types, making visualisations in spreadsheets, critiquing visualisations

Communication
design rules for visualisation, presenting data visualisations, report writing

Business process understanding
data analysis pipeline and how it is used in organisations

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Group assignment	Report and requirement specifications for business analysis task.	15%	n/a
Assignment	An ongoing series of in-lab assessments of spreadsheet skills and visualisations.	25%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Few, Stephen: 2012, Show Me the Numbers, 2nd Ed., Analytics Press, 978-097060197

Other Resources

None