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DATA8011 - Data Mining and Visualisation

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Title:Data Mining and Visualisation
Long Title:Data Mining and Visualisation
Module Code:DATA8011
 
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Data Format
Valid From: Semester 1 - 2020/21 ( September 2020 )
Module Delivered in 1 programme(s)
Module Coordinator: David Goulding
Module Author: David Hawe
Module Description: In this module, the learner will investigate a variety of advanced visualisation concepts and tools for analysing multi-dimensional data, large datasets and complicated process datasets. The creation and use of dashboards will be examined. The learner will also examine data mining - the discovery of patterns and knowledge within large amounts of data. The learner will study a variety of data mining algorithms and models to solve various real-world problems.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the concepts, principles and methods of data mining and data visualisation.
LO2 Apply data explorative and pre-processing techniques to specified datasets and data mining problems.
LO3 Design, implement and communicate appropriate data visualisations and data mining 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 datasets and analytical problems.
LO5 Design and implement appropriate data mining solutions for a specified data mining problem by using a suitable method e.g., algorithm, statistical technique, computer program or mathematical model.
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 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
 

Module Content & Assessment

Indicative Content
Data Mining Overview
Background to data mining. Understanding the differences between data, information and knowledge. Objectives of data mining. Knowledge discovery in databases. Data mining applications - marketing, finance, banking, fraud detection, manufacturing, telecommunications.
Principles and Models of Data Mining
Data mining approaches e.g., CRISP-DM, SEMMA. Categories of data mining problems. Evaluation and interpretation of output patterns. Investigate supervised and unsupervised techniques such as classification, clustering, dependency modelling, sequence modelling, data summarisation, anomaly detection. Matching the model function(s) to the data mining problem at hand.
Data Mining Model Representations
Investigate data mining representations such as decision trees and rules, neural networks, machine learning, case-based reasoning, data visualisation, clustering, hierarchies, self-organised networks, geo-positioning/landscaping.
Data Visualisation Fundamentals
History of data visualisation. Understand the various categories used in the field e.g., infographics and visual analytics. Overview of theory and best practice in these fields, e.g., cognitive amplification, perceptual enhancement and ways to encourage inferential processes.
Advanced Visualisation Techniques
Investigate and implement computer based tools for visualisation, including dashboard creation with RShiny, Tableau or Qlikview. Study how these packages can be connected to data sources, e.g., databases.
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, advanced methods fuzzy clustering, density based and model based clustering.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Explore a process based data set using visualisations and analytics. Present findings in the form of an oral presentation. 1,2,3,4 50.0 Week 8
Project Explore a process data set using machine learning models e.g. Neural Networks or Random Forests. Summarise results in the form of a scientific report. 1,2,4,5 50.0 Sem End
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 institute 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 Theory on course topics and discussion of industry relevant examples. 2.0 Every Week 2.00
Lab Development of practical competency through laboratory-based learning. 2.0 Every Week 2.00
Directed Learning Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory on course topics and discussion of industry relevant examples. 2.0 Every Week 2.00
Lab Development of practical competency through laboratory-based learning. 1.0 Every Week 1.00
Directed Learning Review of lecture notes and recommended material and preparation of reports for selected laboratory sessions and in-class topics. 4.0 Every Week 4.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Jiawei Han, Micheline Kamber, Jian Pei 2012, Data Mining: Concepts and Techniques, 3rd Ed., Elsevier Inc [ISBN: 9780123814807]
  • Rahlf, Thomas 2019, Data Visualisation with R: 111 Examples, 2nd Ed., Springer [ISBN: 978303028442]
Supplementary Book Resources
  • John A Rice 1994, Mathematical Statistics and Data Analysis, 2nd Ed., Duxbury Press [ISBN: 9780495118688]
  • Kjell Johnson, Max Kuhn 2013, Applied Predictive Modelling, 1st Ed., Springer [ISBN: 978-1493979363]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SPRDA_8 Certificate in Process Data Analytics 2 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie