The module will examine the issues involved in selecting data for analysis in the context of Big Data and will consider whether and how techniques associated with Data Mining can be used in a Big Data context. Topics covered will include:
- the definition of data harvesting and its relevance in a Big Data context
- data preparation in a Big Data context (gathering and validating data, evaluating the quality of the data)
- identifying analysis requirements in a Big Data context
-concepts that underpin data mining
- tools and techniques for data mining
- Evaluation of tools and techniques and suitability for use in specified contexts

100% coursework
Practical element weighted at 50%: application of data mining tools and techniques in a data mining environment, using the Weka data mining tool or similar. Covers 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 makes recommendations on the use of data mining in the context of the scenario. Word limit, 1,500 words. This covers learning outcomes 1 and 3.

The VLE will provide supporting learning materials. The module will use a discussion forum to allow students to share ideas and expertise. Tutor support will be available via the discussion forum and Skype and also by email and telephone.

(note that all the text 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.)

Dean J. 2014 Big Data, Data Mining and Machine Learning John Wiley & Sons

Ahlemeyer-Stubbe A. , Coleman S. 2014 A Practical Guide to Data Mining for Business and Industry John Wiley & Sons

Krishnan K 2013 Data Warehousing in the Age of Big Data Morgan Kaufmann

Russell M.A. 2013 Mining the Social Web O’Reilly (selected chapters only)

WEKA
University applications available through the university’s virtual lab

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 Big Data and be able to select the most appropriate tools and techniques for analysis of complex issues. (Analysis, Reflection).