Foundation degrees

Course Information

Business Intelligence and Data Mining

Module summary

Module code: COMP1615
Level: 7
Credits: 15
School: Liberal Arts and Sciences
Department: Computing and Information Sys.
Module Coordinator(s): Mohammed Hassouna


Pre and co requisites

Basic knowledge of statistics and database


The course aims to demonstrate how Business Intelligence (BI) tools, methodologies and techniques can be used to provide intelligent and efficient support for decision making. The course covers three main areas: 1) Data Warehouse 2) DM and Analytics and 3) Visualizations.
It provides students with the tools and techniques to implement and administer complex Business Intelligence solution. It equips students with the skills required to develop creative solutions to business problems using the latest DM and Analytics and Visualizations technologies.

Learning outcomes

On successful completion of this course a student will be able to:

1 Understand the theoretical underpinnings of BI and DM methodologies, architectures, techniques and algorithms.
2 Conduct an audit and analysis of the BI requirements of an organisation and contribute to the planning of a BI project as part of a Knowledge Management.
3 Critically evaluate and select appropriate DM facilities, algorithms/models and apply them and interpret and report the output.
4 Critically appraise the design and implementation of a DM application/technology using test/sample but realistic data sets and modern tools.
5 Integrate intelligent and DM elements into a BI systems development project.

Indicative content

This course will cover a range of subject areas, including but not limited to:

• Introduction to BI and DM: The overall picture of BI and DM; Integrating DM components into BI systems.
• BI analysis and Audit methods and tools; Architectures for BI; Data Warehousing.
Management Information Systems;
• Methodologies for BI projects; CRISP.
• HCI, explanation and visualization.
• Approaches for evaluating the value of BI systems to the business.
• Data preparation/Data cleaning - Dealing with missing values, outliers and erroneous data.
• Machine Learning including booth supervised learning (decision tree, logistic regression and neural network models) and unsupervised learning (cluster and association analyses).
• Models over attributes - Measures of association, the odds-ratio, the correlation matrix.
Mining the cube for associations, the priori algorithm.
• Efficiency, granularity and scalability of BI and DM systems.

Teaching and learning activity

Concepts will be introduced during lectures, and developed during tutorials and lab sessions. Case-study materials will be used when the students are expected to analyse problem situations, present their findings and suggest courses of action. . Feedback will be provided in the end of each lab/tutorial for improvements and further considerations.

University’s VLE and other online tools will be used to 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.


Individual Coursework - 100% weighting, 50% passmark.
Learning Outcomes 1, 2, 3, 4 & 5.
Outline Details - Technical artefact and report to document the design, implementation and evaluation of BI System.

Students are required to pass all elements of summative assessment in order to pass the course.

Formative Assessment - The formative assessment include data analysis process aimed at boosting business performance and ensuring BI skills are transferred for making more informed decisions. Feedback will be provided in the end of each lab/tutorial for improvements and further considerations.