INMR77-Business Intelligence and Data Mining

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Email: y.l.tan@henley.ac.uk

Type of module:

Summary module description:

INMR77 is concerned with using business intelligence and data mining techniques for managerial decision making. Data mining is the process of selection, exploration and analysis of large quantities of data, in order to discover meaningful patterns and rules which in turn, structure business intelligence in context. In another words, data mining converts the raw data into useful knowledge required to support decision-making. 

Aims:

This module aims to provide students the essential data mining and knowledge representation techniques that transforming data into business intelligence. Application areas covered include marketing, customer relationship management, risk management, personalisation, etc. 

Assessable learning outcomes:

On completion of this course, students should be able to: 

• understand the concepts of business intelligence and data mining and its relevant theory and techniques; 


• develop theoretical and practical skills to address different data types for creation of business intelligence in context; 


• understand how and when data mining can be used as a problem-solving technique in business context; 


• design data model and use relevant techniques for data analysis; 


• being aware of current research issues in data mining; 


• acquire hands-on experience in using conventional data mining software, and evaluate its strength and limitations. 

Additional outcomes:

Outline content:

This module will cover the following areas: 

• concepts of business intelligence and data mining 


• overview of various data mining techniques: what is data mining, types of mining, research/open issues in mining; 


• types of data, data cleaning, data integration and transformation, data reduction; 


• classification and predictive modelling; 


• cluster analysis for generating pattern of data and structuring business intelligence; 


• association rule mining and market-basket analysis; 


• text and web mining; 

Brief description of teaching and learning methods:

A range of teaching and learning methods will be employed, but will focus largely on lectures, labs/tutorials, practical assignments, group work and independent supported learning. 

Summative assessment- Examinations:
None

Summative assessment- Coursework and in-class tests:

Assessment will consist of a written coursework assignment (20 pages of A4) (100%) due on week 37 (Summer term, week 4). 

In the coursework assignment, students will be expected to produce a written report which presents the achievements of the learning outcomes. The assignment will provide students an opportunity to communicate critically and concisely their findings (including the model design, and performance evaluation) which demonstrate their extended understanding of the subject.? 

Formative assessment methods:

All lectures will indicate the core material with an introduction to the topics. These are followed by practical classes and labs/tutorials where discussions and exercises on applying the methods and techniques into the given business scenarios and case studies will be carried out. Feedback will be provided at the end of each lab/tutorial for improvements and further considerations.? 

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

Students will be required to obtain a mark of 50% or above based on the coursework. 

Reassessment arrangements:

By re-submission of the coursework. 

Additional Costs (specified where applicable):

Last updated: 8 April 2019