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DATA8004 - DataMining &KnowledgeDiscovery

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Title:DataMining &KnowledgeDiscovery
Long Title:Data Mining & Knowledge Discovery
Module Code:DATA8004
 
Credits: 5
NFQ Level:Advanced
Field of Study: Data Format
Valid From: Semester 2 - 2014/15 ( January 2015 )
Module Delivered in 1 programme(s)
Module Coordinator: AINE NI SHE
Module Author: AINE NI SHE
Module Description: Data mining - the discovery of valuable patterns and knowledge within large amounts of data - has become a popular and interesting interdisciplinary subject in recent years. Since its conception in the early 1990s, the subject has received a huge amount of attention from the research community, the IT industry and beyond. In this module the learner will study a variety of data mining algorithms and models and will investigate how these can be used 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, methods and techniques of data mining and knowledge discovery.
LO2 Apply appropriate data pre-processing and exploration techniques to specified data mining problems.
LO3 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.
LO4 Evaluate, select and interpret patterns and knowledge discovered as a result of applying data mining solutions to specified data mining problems.
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 CIT 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
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
Co-requisites
No Co Requisites 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, discovering knowledge on the Internet. Current state of data mining.
Principles of Data Mining
Data mining process/approaches e.g. Crisp-DM, SEMMA; Categories of data mining problems; Evaluation and interpretation of output patterns.
Data Mining Model Functions
Investigate some of the following supervised and unsupervised techniques: classification, clustering, dependency modelling, sequence modelling, data summarisation, change and deviation analysis/anomaly detection. Matching the model function(s) to the data mining problem at hand.
Data Mining Model Representations
Using a data mining tool to mine the data, investigate some of the following data mining representations: decision trees and rules; neural networks; machine learning; case-based reasoning; data visualisation: clustering, hierarchies, self-organised networks, geo-positioning/landscaping.
Interpretation & Refinement
Interpreting patterns, removing redundant patterns, translating patterns, refining the data mining process based on knowledge learned. Testing and validating the accuracy of the models using various techniques e.g. simple split, k-fold cross-validation, bootstrapping.
Data Mining Software
Using data mining and forecasting software (e.g. SAS, RapidMiner, R, SPSS) to manipulate algorithms, build and test models for a variety of data sets.
Assessment Breakdown%
Course Work50.00%
End of Module Formal Examination50.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Design and implement an appropriate data mining solution for a specified data mining problem. 2,3 25.0 Week 8
Project Evaluate, select and interpret patterns and knowledge discovered as a result of applying a data mining solution to a specified data mining problem. 2,4 25.0 Week 12
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4 50.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

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 2.0 Every Week 2.00
Lab Utilising data mining tools to apply theory covered in lectures 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Application of theory to project 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 2.0 Every Week 2.00
Lab Utilising data mining tools to apply theory covered in lectures 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Application of theory to project 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Jiawei Han, Micheline Kamber, Jian Pei, 2011, Data Mining: Concepts and Techniques, Third Edition [ISBN: 0123814790]
  • Ian H. Witten, Eibe Frank, Mark A. Hall, 2011, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition [ISBN: 0123748569]
Supplementary Book Resources
  • Daniel T. Larose 2004, Discovering Knowledge in Data: an Introduction to Data Mining [ISBN: 0471666572]
  • Richard J. Roiger, Michael W. Geatz, Data Mining: A Tutorial-based Primer [ISBN: 0321223497]
  • Andy Field, Jeremy Miles 2010, Discovering Statistics Using SAS, 1st Ed. [ISBN: 9781849200929]
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 Group Elective 1

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

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