INMB77-Business Intelligence and Data Mining (BIT)

Module Provider: Informatics Research Centre
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Summer term module
Non-modular pre-requisites:
Modules excluded:
Module version for: 2016/7

Module Convenor: Dr Yin Leng Tan


Summary module description:
INMB77 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.

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

Assessable learning outcomes:
On completion of this course, the 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 topics:
• 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;
• business intelligence implementation, integration and emerging trends.

Brief description of teaching and learning methods:
A range of teaching and learning methods will be employed, but will focus largely on lectures, workshops, practical assignments, group work and independent supported learning. It is supplemented by tutorials offered by local tutors in Beijing Institute of Technology during the pre-intensive and post-intensive periods.

Contact hours:
  Autumn Spring Summer
Lectures 16
Seminars 10
Tutorials 4
Practicals classes and workshops 12
Guided independent study 158
Total hours by term 200.00
Total hours for module 200.00

Summative Assessment Methods:
Method Percentage
Written assignment including essay 100

Other information on summative assessment:
Assessment will be by coursework only. 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 design, implementation 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 workshops 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 in the end of each workshop 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:

Length of examination:

Requirements for a pass:
Students will be required to obtain a mark of 50% overall based on the coursework.

Pass criteria - To pass this module, the students must demonstrate their overall knowledge, understanding and the ability to apply concepts and principles of the methods learned to a set of Business Intelligence and Data Mining tasks.

Distinction criteria – To achieve distinction the students must exhibit their original thoughts and critical-analysis ability in problem-solving and solution/model design.

Reassessment arrangements:
Reassessment will be by resubmitting the failed coursework.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 21 December 2016

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