Module Details

Module Code: DATA8008
Title: Data Visualisation & Analytics
Long Title: Data Visualisation & Analytics
NFQ Level: Advanced
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: Data visualisation is of growing interest in the field of data science and analytics. In this module, the learner will investigate a variety
of advanced visualisation concepts and tools for analysing multi-dimensional data, large data sets and geospatial data. The use and creation of dashboards with be examined. Data visualisation theory will be appraised. The learner will also examine major statistical modelling trends and challenges within the field of data science and analytics.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe the concepts, principles and methods of data visualisation.
LO2 Apply data explorative and pre-processing techniques to specified datasets.
LO3 Design, implement and communicate appropriate data visualisation techniques to solve data analytical problems.
LO4 Interpret and communicate patterns and knowledge discovered as a result of applying data visualisation and analytical techniques to data sets and analytical problems.
LO5 Assess a variety of data analytics solutions to current challenges in the area.
Dependencies
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 MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules 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
 
Indicative Content
Data Visualisation Fundamentals
History of data visualisation. Understand the various categories used in the field e.g. Information/data/scientific visualisation, infographics, visual analytics. Overview of theory and best practice in these fields, e.g. cognitive amplification, perceptual enhancement and ways to encourage inferential processes.
Data visualisation pre-processing techniques
Learn data cleaning techniques relevant to data visualisation - data aggregation, data sampling, impute missing data, find inconsistencies. Learn transformation techniques - data normalisation, construct new variables, Investigate how to use regular expressions and data manipulation techniques to pre-process data sets. Implement these processes using R, Excel or similiar computer package.
Advanced visualisation techniques
Investigate and implement computer based tools for visualisation, e.g. dashboard creation with RShiny, Tableau/Qlikview; how these packages can be connected to data sources, e.g. databases.
Geographic Information Systems (GIS)
Investigate and implement GIS software, e.g. QGIS, R, ArcGIS; examine and discuss their features - interactivity, panning, zooming; browser based implementations.
Visualisation and Analytics
Examine a variety of visualisation, analytical and statistical modelling methods that are used to solve data mining and data analytics problems, e.g. anomaly detection, pattern discovery, network analysis. Investigate clustering techniques e.g. partitioning methods, hierarchical clustering and advanced methods - fuzzy clustering, density based and model based clustering.
Data Analytics Techniques
Investigate the main pitfalls in data visualisation and data analytics in a real-world setting. Compare and contrast various data analytics techniques.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 8 Learning Outcomes 1,2,3,4
Assessment Description
Design and implement a visualisation and/or analytical technique to solve a problem.
Assessment Type Project % of Total Mark 50
Timing Week 12 Learning Outcomes 1,2,3,4,5
Assessment Description
Evaluate and implement a visualisation technique to solve a problem; research and communicate a data analytics topic.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University 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 Theory, algorithms and models of data visualisation and analtyics' techniques. Every Week 2.00 2
Lab Contact Data visualisation and analytics laboratory - application and implementation of theory covered in lectures. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Application of theory to project Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Theory, algorithms and models of data visualisation and analtyics' techniques. Every Week 1.50 1.5
Lab Contact Data visualisation and analytics laboratory - application and implementation of theory covered in lectures. Every Week 1.50 1.5
Independent & Directed Learning (Non-contact) Non Contact Application of theory to project Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Ben Fry. (2007), Visualizing Data, O'Reilly Media, Sebastopol, CA, [ISBN: 0596514557].
  • Nathan Yau. (2011), Vizualise This, Wiley, [ISBN: 0470944889].
  • Matloff, Norman. (2011), The Art of R Programming, No Starch Press, San Francisco, [ISBN: 9781593273842].
  • Kabacoff, Robert I.. (2015), R in Action, 2nd. Manning, New York, p.580, [ISBN: 9781617291388].
  • Kassambara, Alboukadel. (2017), Practical Guide to Cluster Analysis in R, CreateSpace Independent Publishing Platform, [ISBN: 1542462703].
Supplementary Book Resources
  • Jiawei Han, Micheline Kamber, Jian Pei. (2011), Data Mining: Concepts and Techniques, Morgan Kaufmann, p.740, [ISBN: 9780123814807].
  • Kotu, Vijay and Deshpande, Bala. (2015), Predictive Analytics and Data Mining, Morgan Kaufmann, Elsevier, MA, USA, p.426, [ISBN: 9780128014608].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Mandatory