Long Title:Data Mining and Visualisation
Language of Instruction:English
Module Code:H8DMV
Credits: 5
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 1 programme(s)
Module Coordinator: Simon Caton
Module editor: Simon Caton
Teaching and Learning Strategy: The learning strategy involves the use of lectures, and practical work in the form of method seminars and workshops. Learners will also have access to web based support.
Learning Environment: Learning will take place in a computer lab environment with access IT resources. Learners will have access to library resources, both physical and 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: This module focuses on introducing learners to the fundamentals of data analytics and visualisation in the domain of IoT.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Propose and Apply fundamental data mining methodologies such as KDD to IoT data sets.
LO2 Evaluate the application of data mining methods to IoT data.
LO3 Assemble representative visualisations of IoT data to derive and identify contextual understanding.
LO4 Generalise and Interpret IoT data through the application and evaluation of data mining and visualisation techniques.
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
• Data Mining Methodologies (e.g. KDD, CRISP-DM, and SEMMA) • Exploration and visualisation of sample datasets • Fundamentals of Visualisation (e.g. Weber's Law, Steven's Power Law, Gestalt Principles, Tufte's Principles of Information Design)
Data Transformation
• Attribute selection and discretization • Sampling methods • Data cleaning • Dimensionality Reductions (e.g. Principle Component Analysis and Linear Discriminant Analysis)
Data Mining and Machine Learning
• Model Evaluation • Classification Schemes (e.g., Decision Trees, Bayesian Methods, Support Vector Machines, kNN) • Frequent Pattern Mining (e.g. Association Rule Mining) • Hidden Markov Models and Artificial Neural Networks • Clustering Methods
Visualisation Techniques
• Planning and Designing Visualisations • Visualisation Tools
Assessment Breakdown%
Coursework100.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Open-book Examination Learners are presented with a series of IoT data sets and/or hypothetical data sets. They should, as a group, devise, document and orally defend a proposed data analytics methodology for each scenario. 1,2 20.00 n/a
Major Project Learners should choose and acquire a dataset related to the IoT domain, develop, and document a process for preparing and analysing the data through to implementing a number of data visualizations. They should then analyze the results and provide a comparative evaluation of the different data mining and visualization methods leveraged in the project. 2,3 60.00 n/a
Presentation Learners should present the results of their project in a non-technical context, focusing on the code distillation of their applied methodology, core results and take aways from the project. 4 20.00 n/a
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat failed items
The student must repeat any item failed

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 24 Every Week 24.00
Lab No Description 24 Every Week 24.00
Total Hours 48.00
Total Weekly Learner Workload 48.00
Total Weekly Contact Hours 48.00
This module has no Part Time workload.
 

Module Resources

Recommended Book Resources
  • Matthew Ward, Georges Grinstein, Daniel Keim, Interactive Data Visualization, A K Peters Ltd [ISBN: 9781568814735]
  • Robert Stackowiak, Art Licht, Venu Mantha, and Louis Nagode 2015, Big Data and The Internet of Things: Enterprise Information Architecture for A New Age, 1 Ed., Apress [ISBN: 9781484209875]
Supplementary Book Resources
  • Markus Hofmann and Ralf Klinkenberg 2013, RapidMiner: Data Mining Use Cases and Business Analytics Applications, 1 Ed., Chapman and Hall/CRC [ISBN: 9781482205497]
  • Vijay Kotu and Bala Deshpande 2014, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner, 1 Ed., Morgan Kaufmann [ISBN: 9780128014608]
  • Brett Lantz 2015, Machine Learning with R, 2 Ed., Packt Publishing [ISBN: 9781784393908]
Recommended Article/Paper Resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
BSHC BSc (Honours) in Computing 8 Optional