Module Details

Module Code: n/a
Full Title: Data Analysis and Visualisation
Valid From:: Semester 2 - 2022/23 ( January 2023 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner:: Seamus Rispin
Departments: Unknown
Module Description: The aim of this module is to enable learners to effectively use data analysis and visualisation to both explore data and communicate findings. On completion of this module, learners will be able to perform advanced multivariate analysis and be able to implement Data-Visualisation solutions using appropriate technologies.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Evaluate the field of data analytics, demonstrating an understanding of its role in organisational decision making.
MLO2 Choose the appropriate clustering, classification and ordination techniques in the analysis of large data sets, interpret the results and draw conclusions.
MLO3 Employ self-service business intelligence tools and techniques for requirements elicitation and analysis to make evidence-based decisions to drive business change and improve business processes.
MLO4 Apply principles of visualization theory. Plan and present findings of problem-solving efforts.
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
Introduction to Business Analysis & Data Analytics
Role in industry, key concepts, global trends, knowledge areas, tasks, techniques, competencies, terminology, big data, advantages & challenges of big data.
Business Intelligence & Organisational Strategy
Alignment of business intelligence & organisational strategy, data analytics as a core business tool, data analytics as a complementary business tool, exposure to real-world implementations in business.
Data Analysis Applications
Using software applications such as tableau and or Power BI to import or enter data, clean data, analyse data
Data Visualisation
Present data outcomes through the use of common graphics. Examples of graphics shall include charts, plots and infographics. It may also include animations (if feasible).
Module Assessment
Assessment Breakdown%
Course Work100.00%
Module Special Regulation
 

Assessments

Full-time

Course Work
Assessment Type Written Report % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 3,4
Duration in minutes 0
Assessment Description
Candidates will be required to use Business Intelligence tools to implement analytical techniques, analyse the data and report on the findings.
Assessment Type Continuous Assessment % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing n/a Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Assessment will involve the design and development of analytical models and case study analysis and reporting. Students will be assessed on their ability to understand, articulate and frame the problem.
No Project
No Practical
No Final Examination

Part-time

Course Work
Assessment Type Continuous Assessment % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing S1 Week 6 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Assessment will involve the design and development of analytical models and case study analysis and reporting. Students will be assessed on their ability to understand, articulate and frame the problem.
Assessment Type Written Report % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 3,4
Duration in minutes 0
Assessment Description
Candidates will be required to use Business Intelligence tools to implement analytical techniques, analyse the data and report on the findings.
No Project
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
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 There will be one 2-hour lab timetabled class per week. 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 & exercises. 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
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description Every Week 1.00 1
Practical Contact No Description Every Week 1.00 1
Directed Reading Non Contact No Description Every Week 3.00 3
Independent Study Non Contact No Description Every Week 2.00 2
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • Andy Kirk. (2016), Data Visualisation, SAGE Publications Limited, p.368, [ISBN: 9781473912144].
  • Thomas J. Quirk,Eric Rhiney. (2016), Excel 2016 for Marketing Statistics, Springer, p.0, [ISBN: 331943375X].
Supplementary Book Resources
  • Harvard Business Review. (2018), HBR Guide to Data Analytics Basics for Managers, Harvard Business School Press, p.256, [ISBN: 1633694283].
  • Cole Nussbaumer Knaflic. (2015), Storytelling with Data: A Data Visualization Guide for Business Professionals, John Wiley & Sons, [ISBN: 978-111900225].
  • Bernard Marr. (2016), Big Data in Practice, John Wiley & Sons, p.320, [ISBN: 1119231388].
This module does not have any article/paper resources
Other Resources