Module Details

Module Code: DATA C9003
Full Title: Data Visualisation and Insight
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner:: Kevin McDaid
Departments: Unknown
Module Description: This module will enable the student to develop the advanced technical, critical thinking and communication skills required to explore and present data to deliver valued insights to a targeted audience within the context of a data analytics project lifecycle.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Research, identify and evaluate the key insights required to deliver value for a targeted set of stakeholders related to a data analytics project.
MLO2 Devise, implement and critique appropriate visualisation techniques for data exploration within a data analytics process.
MLO3 Devise, implement and critique appropriate data visualisation theory and techniques to communicate results and insights to stakeholders.
MLO4 Effectively present and defend the validity of key insights at the final stage of a data analytics project.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT 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
 
Module Indicative Content
Data Analytics Processes
Review of lifecycle and processes (CRISP-DM, SEMMA). Types of problems. Role of stakeholders. Statement of problem and related business value. Case studies with guest presentations in areas such as sports analytics.
Visualisation Theory
Overview of visualisation including Information/data/scientific visualisations and infographics. Static, dyanamic and interactive visualisations. Exploratory vs explanatory visualisation. Data types. High dimensional data. Hierarchical data. Principles of visualisation design (perception, colour, font). Data density. Data ink maximisation and graphical design. Colour theory.
Visualisation Techniques for Data Exploration and Explanation
Exploration of high dimensional data. Multidimensional scaling. Data preprocessing and cleaning. Exploration of outliers and missing values. Visualisation methods and technologies for structured and unstructured data types such as temporal, geospatial, network, hierarchical and text data. Charts(number and graphical), plots, maps, diagrams, cluster maps, matrices, trees and network diagrams. Simulations. Immersive data analytics. Dealing with big data. Application through appropriate programming, spreadsheet and BI technologies.
Communication
Reporting and presenting. Effective visualisation. Visualisation and presentation tools. Spreadsheets. Dashboards. Business Intelligence tools. Interactive visualisations. Case studies.
Module Assessment
Assessment Breakdown%
Project100.00%
Module Special Regulation
 

Assessments

Full Time On Campus

No Course Work
Project
Assessment Type Group Project % of Total Mark 40
Marks Out Of 0 Pass Mark 0
Timing S1 Week 6 Learning Outcome 1,3
Duration in minutes 0
Assessment Description
Working in teams the learners will identify and document a real analytics problem, describe the data requirements, characterise the stakeholders and establish a set of key insights that would deliver business value. They would then design and critically evaluate a set of potential visualisations that would effectively communicate these insights. The learners would prepare a report on their work and would present these proposed visualisations to the class group and external participants. Throughout the project each learner would keep a diary and at the end of the project would formally reflect on their work.
Assessment Type Project % of Total Mark 60
Marks Out Of 0 Pass Mark 0
Timing S1 Week 13 Learning Outcome 2,3,4
Duration in minutes 0
Assessment Description
The learner will take a data analytics problem with real data and develop, using suitable methods and technologies, a set of visualisations to communicate key insights. The learner will present and discuss their work in front of an external audience and will critcally evaluate their outputs and performance through a reflective journal.
No Practical
No Final Examination

Part Time On Campus

No Course Work
Project
Assessment Type Group Project % of Total Mark 40
Marks Out Of 0 Pass Mark 0
Timing S1 Week 6 Learning Outcome 1,3
Duration in minutes 0
Assessment Description
Working in teams the learners will identify and document a real analytics problem, describe the data requirements, characterise the stakeholders and establish a set of key insights that would deliver business value. They would then design and critically evaluate a set of potential visualisations that would effectively communicate these insights. The learners would prepare a report on their work and would present these proposed visualisations to the class group and external participants. Throughout the project each learner would keep a diary and at the end of the project would formally reflect on their work.
Assessment Type Project % of Total Mark 60
Marks Out Of 0 Pass Mark 0
Timing S1 Week 13 Learning Outcome 2,3,4
Duration in minutes 0
Assessment Description
The learner will take a data analytics problem with real data and develop, using suitable methods and technologies, a set of visualisations to communicate key insights. The learner will present and discuss their work in front of an external audience and will critcally evaluate their outputs and performance through a reflective journal.
No Practical
No Final Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.

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

 

Module Workload

Workload: Full Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Deliver theory, principles and paradigms. Every Week 1.00 1
Practical Contact In these lecture/practical classes, the delivery of new material will be integrated with the practical implementation of that material. Every Week 2.00 2
Directed Reading Non Contact Guided preparation, reading & project work. Every Week 2.00 2
Independent Study Non Contact Students will be expected to conduct independent review of content & research related topics. Involving both theoretical & practical aspects of content. Every Week 3.00 3
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
Workload: Part Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Deliver theory, principles and paradigms. Every Week 1.00 1
Practical Contact In these lecture/practical classes, the delivery of new material will be integrated with the practical implementation of that material. Every Week 2.00 2
Directed Reading Non Contact Guided preparation, reading & project work. Every Week 2.00 2
Independent Study Non Contact Students will be expected to conduct independent review of content & research related topics. Involving both theoretical & practical aspects of content. Every Week 3.00 3
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Kieran Healy. (2018), Data Visualization: A Practical Introduction, Princeton University Press, [ISBN: 0691181624].
  • Claus O. Wilke. (2019), Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O'Reilly Media, [ISBN: 1492031089].
  • Hadley Wickham. (2016), ggplot2:Elegant Graphics for Data Analysis, Springer International Publishing, [ISBN: 978-3-319-242].
  • Edward R. Tufte. (2001), The Visual Display of Quantitative Information, Graphics Press, [ISBN: 978-193082413].
  • Julie Steele (Editor), Noah Iliinsky (Editor). (2010), Beautiful Visualization: Looking at Data through the Eyes of Experts (Theory in Practice), O'Reilly Media, [ISBN: 9781461471370].
  • Cole Nussbaumer Knaflic. (2015), Storytelling with Data: A Data Visualization Guide for Business Professionals, John Wiley & Sons, [ISBN: 978-111900225].
This module does not have any article/paper resources
Other Resources