INMR77: Business Intelligence and Data Mining

Module code: INMR77

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 1

Module convenor: Dr Yin Leng Tan, email: y.l.tan@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 28 May 2024

Overview

Module aims and purpose

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 transforms/coverts the raw data into useful information/knowledge required to support decision-making.  

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.  

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

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

1. Demonstrate an understanding of the concepts of business intelligence and data mining and its relevant theory and techniques 

2. Demonstrate an understanding of how and when data mining can be used as a problem-solving technique in a range of organisational contexts 

3. Develop theoretical and practical skills to address different data types for creation of business intelligence in a range of organisational contexts. 

4. Apply appropriate data mining process frameworks and use relevant data mining techniques and methods for data modelling and business solution design. 

Module content

This module will cover the following areas: 

1. concepts of business intelligence and data mining 

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

3. types of data, data cleaning, data integration and transformation, data reduction; 

4. classification and predictive modelling; 

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

6. association rule mining and market-basket analysis; 

7. concepts of text and web mining.  

Structure

Teaching and learning methods

A range of teaching and learning methods will be employed including exercises, tutorials, videos and case studies, but will focus largely on lectures, in-class lab tutorials/seminars, practical assignments, group work and independent supported learning to enable students to learn and practice the tools and techniques.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

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

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

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 at the end of each lab/tutorial for improvements and further considerations.

Reassessment

Additional costs