module specification

This module provides an appreciation of data mining concepts, techniques, and process for business Intelligence. It covers data mining techniques for both supervised learning (decision tree, logistic regression and neural network models) and unsupervised learning (cluster and association analyses).
It is designed to help equip the students with practical skills in applying data mining techniques in a modern business environment.

Basic knowledge of statistics and database

The aims of this module are to:

• provide students with an understanding of key data mining concepts,techniques and processfor business Intelligence.
• appreciate the purpose and breadth of areas of application of data mining
• understand and compare the techniques and tools available for solving data mining problems
• develop students with practical skills in applying data mining techniques for business Intelligence.

• Concepts and fundamentals of data mining and business intelligence
• Data mining process:   cross Industry standard processing (CRISP) for data mining
• Data preparation and graphical exploration: visualising large data sets, data cleaning, outlier detection, variable transformation
• Prediction and classification methods for business Intelligence: decision trees, logistic regression and neural network models
• Mining relationships among records: cluster analysis,  association analysis (‘market basket analysis’)
• Forecasting time series for business intelligence
• Model evaluation and predictive performance

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:
- Lecture (2 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies
- Workshop (2 hour / week):
Data mining technical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful interactive mining package (e.g. SAS).
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- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.

LO1 appreciate the objectives and usefulness of data mining technology for businesses
LO2 understand the key concepts, process and components of data mining
LO3 describe and utilise a range of data mining techniques for business applications
LO4 appreciate the strengths and limitations of various data mining models/tools
LO5 be aware of the latest developments in data mining, particularly in business Intelligence
LO6 apply data mining techniques in a real-world business intelligence context.

1. Han, J., Kamber, M. & Pei, J. (2006) Data Mining: Concepts and Techniques, Second Edition. 2nd ed. Morgan Kaufmann.
2. Linoff, G.S. & Berry, M.J. (2011) Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. 3rd ed. John Wiley & Sons.
3. Matignon, R. (2007) Data Mining Using SAS Enterprise Miner. Wiley-Blackwell.
4. Shmueli, G., Patel, N.R. & Bruce, P.C. (2010) Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel(r) with XLMiner(r). 2nd ed. Wiley-Blackwell.