1) Understand, discuss and be able to critically evaluate the issues involved in sourcing, preparing and making available data for analysis (Data harvesting).
Knowledge and Understanding


2) Systematically understand the concepts that underpin data mining and be able to apply relevant tools and techniques to solve complex problems.
Knowledge and Understanding
Application

3) Critically evaluate data mining approaches in the context of Business data and be able to select the most appropriate tools and techniques for analysis of complex issues.
Analysis
Reflection

4) Critically identify and discuss appropriate strategies for modelling and analysing business data. Knowledge and Understanding
Problem solving

This module will examine the issues involved in selecting data for analysis in the context of Business data and Big Data and will consider whether and how techniques associated with Data Mining can be used to enhance business functions.
Topics covered will include:
- The definition of data harvesting and its relevance in a Big Data context
- Data preparation in a Business Data context (gathering and validating data, and evaluating the quality of the data)
- Identifying analysis requirements in a Business context
- Concepts that underpin data mining
- Tools and techniques for data mining
- Evaluation of tools and techniques and suitability for use in specified contexts
- Introduction to Business Data Analytics
- Data Analytics Lifecycle
- Clustering
- Association Rules
- Regression
- Classification
- Time Series Analysis
- Text Analysis
- Data visualisation techniques

100% coursework

Practical element weighted at 50%: application of data mining tools and techniques in a data mining environment, using for example the Weka data mining tool or similar (Learning Outcomes 1 and 2).

Report: a Management style report weighted at 50%, based on a scenario, which identifies the issues involved in data harvesting and data modelling which makes recommendations on the use of these areas in the context of the scenario. Word limit, 1,500 words (Learning Outcomes 1, 3 and 4).

The module uses 12 hours of formal lectures, and 24 hours of workshop based teaching which will include practical work, seminars and theoretical material. Extensive use is made of the VLE and of formative assessment.

Note that all the texts listed here are available through the university e-books service. It is not expected that students will buy these texts. The nature of the subject means that these texts will be constantly updated.

The following is sourced from the University Library

Dean J., (2014), Big Data, Data Mining and Machine Learning, John Wiley & Sons , ISBN 9781118920395.

Ahlemeyer-Stubbe A. et al, (2014), A Practical Guide to Data Mining for Business and Industry, John Wiley & Sons, ISBN 1119977134.

Krishnan, K. (2013), Data Warehousing in the Age of Big Data, Morgan Kaufmann, ISBN 9780124059207.

Russell, M. A., (2013), Mining the Social Web Reilly, O’Reilly, ISBN 9781449368227.

Dietrich, D. et Al. , (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons, ISBN-13: 978-1118876138.

Holmes, D. E. (2017), Big Data: A Very Short Introduction (Very Short Introductions), OUP Oxford, ISBN-10: 0198779577.

Open source data analytics tools such as
WEKA

None

The module will introduce you to the issues involved in identifying business data for analysis and modelling and will look at data mining in the context of Big Data and Business Data and will give you hands on experience of working with data mining tools and techniques.

The module uses 13 hours of formal lectures, and 26 hours of workshop based teaching which will include practical work, seminars and theoretical material. Extensive use is made of the VLE and of formative assessment.