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: 2022/3

Module Convenor: Dr Yin Leng Tan
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.Ìý

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.Ìý

Assessable learning outcomes:

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

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


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


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


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


• being aware of current research issues in business intelligence andÌýdata mining;Ìý


• acquire hands-on experience in using conventional data miningÌýsoftware, andÌýevaluate its strength and limitations.Ìý

Additional outcomes:

Outline content:

This module will cover the following areas:Ìý

• concepts of business intelligence and data miningÌý


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


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


• classification and predictive modelling;Ìý


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


• association rule mining and market-basket analysis;Ìý


• concepts of text and web mining;Ìý

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.Ìý

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		10
Seminars		5
Tutorials		10
Practicals classes and workshops		5
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		30
Ìý Ìý Wider reading (directed)		20
Ìý Ìý Advance preparation for classes		10
Ìý Ìý Preparation for tutorials		40
Ìý Ìý Preparation of practical report		70
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	200	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Report	100

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 37Ìý(Summer term, week 4).Ìý

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.?Ìý

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 data sets will be carried out. Feedback will be provided atÌýthe end of each lab/tutorial for improvements and further considerations.?Ìý

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy £Penalties for late submission for Postgraduate Flexible programmes£, which can be found here: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

Students will be required to obtain a mark of 50%Ìýor aboveÌýbased on the coursework.Ìý

Reassessment arrangements:

ByÌýre-submission ofÌýthe coursework.Ìý

Additional Costs (specified where applicable):

Cost	Amount
1. Required text books	£50.00

Ìý

Last updated: 22 September 2022