Module Details

Module Code: DATA8003
Title: Unstructured Data & Visualis'n
Long Title: Unstructured Data & Visualis'n
NFQ Level: Advanced
Valid From: Semester 1 - 2017/18 ( September 2017 )
Duration: 1 Semester
Credits: 5
Field of Study: 4816 - Data Format
Module Delivered in: 1 programme(s)
Module Description: In data visualisation, data will be investigated using various visualisation and modelling techniques. More advanced visualisation concepts and tools for analysing multi dimensional data, large data sets and geospatial data will also be examined.
In unstructured data analysis, the learner will examine how to organise and analyse both text based data forms and other unstructured data (e.g. web logs, web content, twitter). The learner will investigate the characteristics of unstructured data and how challenges in the area can be overcome using a variety of descriptive and analytical techniques.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Investigate data using various visualisation and modeling theories and techniques.
LO2 Use a variety of visualisation techniques and tools to solve data mining and analytics problems.
LO3 Make observations and build a body of evidence to support a case or project.
LO4 Organise and analyse non-numerical or unstructured data.
LO5 Examine relationships in data; and combine analysis with linking, searching and modelling.
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).
13372 DATA8003 Unstructured Data & Visualis'n
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 concepts
Understand the various categories used in the field e.g. Information/data/scientific visualisation, infographics, visual analytics.
Data Visualisation traditional statistical approaches
Histograms, boxplots, scatter plots; Analysing correlations and patterns between variables. Univariate, bivariate and multivariate ways of presenting data.
Advanced visualisation techniques
Investigate computer based tools for visualisation and their features - dashboards, drop-down menus, interactivity. Use software to display the data e.g. R Shiny, Qlikview, Tableau, Rapidminer.
Unstructured data
Understand the characteristics of unstructured data and how this impacts on analysis.
Organise data
Using a variety of software (e.g. R, Rapidminer, NVivo) to gather (e.g. web-scrape) and organise unstructured data and explore its characteristics. Investigate various data cleaning techniques so that the data can be analysed further.
Explore data
Use statistical and data mining techniques to create models so that meaningful analyse can be performed on unstructured data, e.g.TF-IDF, bag of words, concepts; as well as K-means clustering, K-Nearest Neighbour, Bayesian inference.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 25
Timing Week 4 Learning Outcomes 1,2,3
Assessment Description
Lab examination in visualisation.
Assessment Type Project % of Total Mark 25
Timing Week 8 Learning Outcomes 1,2,3
Assessment Description
Analyse a data set using appropriate visualisation techniques.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 3,4,5
Assessment Description
Analyse an unstructured data set and write a report on the findings.
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 Formal lecture Every Week 2.00 2
Lab Contact Laboratory sessions Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes and preparing for labs 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 Formal Lecture Every Week 2.00 2
Lab Contact Laboratory sessions Every Second Week 1.00 2
Independent & Directed Learning (Non-contact) Non Contact Review of lecture notes and preparing for labs Every Week 4.00 4
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Julie Steele, Noah Iliinsky,. (2011), Designing Data Visualizations, [ISBN: 1449312284].
  • Nathan Yau,. (2011), Visualize This, [ISBN: 0470944889].
  • Ben Fry. (2007), Visualizing data, O'Reilly Media, Sebastopol, CA, [ISBN: 0596514557].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_BBISY_8 Bachelor of Business (Honours) in Information Systems 7 Elective