DCU Home | Our Courses | Loop | Registry | Library | Search DCU

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Processing and Visualisation
Module Code CA273A
School School of Computing
Module Co-ordinatorSemester 1: Suzanne Little
Semester 2: Suzanne Little
Autumn: Suzanne Little
Module TeachersSuzanne Little
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Coursework Only

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.

Workload Full-time hours per semester
Type Hours Description
Lecture24No Description
Online activity10Portfolio preparation
Independent Study133.5Self-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

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

Assessment Breakdown
Continuous Assessment100% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentDemonstrate basic git skills by cloning, editing and updating a project including a report on how to use git.9%Week 3
Loop QuizSynchronous 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
AssignmentThrough ongoing lab assessments, load, transform and clean a given data set or sets producing a suitable report documenting the process.12%n/a
Loop QuizSynchronous 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
AssignmentDesign 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
AssignmentVisualisation 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 journalWrite 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;
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 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

Programme or List of Programmes
DSBSc in Data Science
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)

My DCU | Loop | Disclaimer | Privacy Statement