Long Title:Data Visualization
Language of Instruction:English
Module Code:H8DV
Credits: 5
NFQ Level:LEVEL 8
Field of Study: Computer use
Module Delivered in 1 programme(s)
Module Coordinator: ANTHONY PAUL STYNES
Module editor: Maurice Fitzgerald
Teaching and Learning Strategy: Teaching & Learning may take place via lectures, lab work, case studies, class discussions and project work. This module is suitable for blended delivery. Techniques such as flipped classroom and online videos showing step by step instructions, links to extra material available on the Internet, Moodle forum may be used. Learner’s may also use collaborative tools for the development of the project. The project submission will be done online. Where appropriate, the use of github will be encouraged as a collaborative tool to enable students to work on the development of a project. It will also improve the transparency of projects for markers as it will enable them to clearly identify the work carried out by students as part of the project.
Learning Environment: Learning will take place in both a classroom and computer laboratory environment with access to IT resources. Learners will have access to library resources, both physical & electronic and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the College’s virtual learning environment.
Module Description: The aim of this module is to enable learners to effectively utilise computer-based data visualization techniques and strategies to communicate information. Learners will be able to analyse and present simple-intermediate data as information through the application of design principles and interaction strategies for data visualization. Furthermore, learners will be able to critically assess and evaluate different data types, information requirements, and data visualization tools so as to provide clear, effective, and engaging graphical information representations.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Analyse the theory and concepts relating to visualization design and data representation.
LO2 Analyze and distinguish between visualization techniques for specific problems to enable effective communication of data analysis.
LO3 Design, develop, and implement processes for data visualization.
LO4 Propose a suitable visualization design for a particular combination of data characteristics and application.
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 

Module Content & Assessment

Indicative Content
Introduction
 What is Data Visualization?  Characteristics of Data, Data Types and Information  Communication through visualization
Visualization Design
 Principles of data visualization  Graphical integrity  Clarity of data representation  Elements of visual design (layout, colour, fonts, labelling etc.)
Data Visualizations
 Vector fields and flow data  Time-varying data  High-dimensional data: dimension reduction, parallel coordinates  Non-spatial data: multi-variate, tree/graph structured, text
Evaluation of Visualization Methods
 Small and large data sets  Suitable visualization design  Data and application characteristics
Applications of Visualization
 Scientific, medical, mathematical data  Flow visualization  Spatial Analysis
Assessment Breakdown%
Coursework100.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project (0050) Learning outcomes may be assessed through a project in which learners must choose and acquire a set of raw data; design, develop, and document a process for preparing the data through to implementing an interactive or a number of static data visualizations; analyse the results; and provide an evaluation of the correct use of data and visual techniques that were implemented. Student must then present their project work. 2,3,4 40.00 n/a
Continuous Assessment (0200) Students are given small sample data sets and are required to work together to develop a basic visualization of some feature of that dataset. Basic domain information will be provided. Complexity of lab assessments will scale appropriately with student learning. 1,2,3,4 60.00 n/a
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Learners who fail this module will be required to sit a repeat module assessment where all learning outcomes will be examined.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 12 Every Week 12.00
Tutorial No Description 12 Every Week 12.00
Independent Learning No Description 101 Every Week 101.00
Total Hours 125.00
Total Weekly Learner Workload 125.00
Total Weekly Contact Hours 24.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 12 Every Week 12.00
Tutorial No Description 12 Every Week 12.00
Independent Learning No Description 101 Every Week 101.00
Total Hours 125.00
Total Weekly Learner Workload 125.00
Total Weekly Contact Hours 24.00
 

Module Resources

This module does not have any book resources
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
HDSDA Higher Diploma in Science in Data Analytics 2 Optional