Module: Data Processing and Visualisation

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Processing and Visualisation
Module Code	CA273A (ITS) / CSC1158 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Suzanne Little
Module Teachers	-
NFQ level	8	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

Description

This module will equip students with knowledge of methods for processing, ingesting, cleaning and reformatting data sets using a variety of tools. It will introduce exploratory data analysis through interactive visualisation and develop student skills in creating effective data visualisations. The module will enable students to develop skills in communication, visualisation design and the ability to critique the effectiveness of data visualisations.

Learning Outcomes

1. Explain the data analysis pipeline and the stages of data processing, transformation, cleaning, management, visualisation and communication.
2. Identify and describe relevant data formats and standards.
3. Select and perform appropriate data cleaning operations using different tools and techniques and documenting the processes (e.g., notebooks).
4. Design and create effective interactive data visualisations using both dedicated applications (e.g., Tableau, Spreadsheets) and developer libraries (e.g., matplotlib, seaborn, bokeh).
5. Understand the requirements for communicating data analysis through visualisations and critique both their own and other visualisations.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	No Description
Laboratory	20	Computer-based
Online activity	10	Portfolio preparation
Independent Study	133.5	Self-directed skills practise and assignments
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 processing
data loading, formats, metadata, conversion, basic APIs to ingest data, using notebooks, standards

Data cleaning
identifying possible errors, handling null values, geolocation issues, tools to clean data set

Data visualisation
selecting appropriate graph types, creating interactive visualisations, using common visualisation tools, critiquing visualisations

Communication
design rules for visualisation, creating data exploration notebooks recording processes, presenting data visualisations

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	Demonstrate basic git skills by cloning, editing and updating a project including a report on how to use git.	9%	Week 3
Loop Quiz	Synchronous Online Quiz via loop on the main concepts from weeks 1 to 3 on the data analytics pipeline, data types and formats and data standards.	12%	Week 5
Assignment	Through ongoing lab assessments, load, transform and clean a given data set or sets producing a suitable report documenting the process.	12%	n/a
Loop Quiz	Synchronous Online Quiz via loop on the content from weeks 5-8 including creating a simple graph, explaining big data, the Internet and communication theory.	12%	Week 9
Assignment	Design and produce a significant and interactive data visualisation demonstrating both technical and communication skills. Produce the visualisation (as a screen capture, app or web site), a report and give a presentation on the visualisation.	20%	Week 10
Assignment	Visualisation Critique: review both provided visualisations and peer submissions for the visualisation assignment and write a brief analysis of their use of visualisation principles.	20%	Week 12
Reflective journal	Write a set of skill summaries and self-assessments from the technologies used in the module.	15%	Week 12

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

Field Cady: 2017, The Data Science Handbook,
Stephen Few: 2012, Show Me the Numbers, 2nd Ed,
Andy Kirk: 2016, Data Visualisation,
Cole Nussbaumer Knaflic: 2015, Storytelling with Data: A Data Visualization Guide for Business Professionals,
Sinan Ozdemir: 2016, Principles of Data Science,

Other Resources

None