Aims and Fit of Module

This module aims to provide students with an in-depth understanding of the key technologies of Data Mining and Big Data Analytics. It covers not only classical data mining techniques but also techniques in addressing issues and challenges raised in Big Data Analytics by introducing scalable parallel data mining algorithms which can be executed on computer clusters, including data stream mining techniques and algorithms for the analysis of high velocity data as well as software systems used for Data Mining and Big Data Analytics.


Because of the specialist knowledge and techniques the module provides, it serves as a required module for MSc Applied of informatics.  As module provides common computing skills, it serves as an optional module for MRes Computer Science, MSc Social Computing and MSc Financial Computing.

Learning outcomes

A Demonstrate a systematic and critical understanding of the
challenges of Data Mining and Big Data Analytics;
B. Use advanced algorithmic knowledge and key techniques to tackle
the challenges of Data Mining and Big Data Analytics. Discuss the
limitations of the techniques employed;
C. Show expertise in using state-of-the-art software tools to
implement solutions to Data Mining and Big Data Analytics. Discuss
the limitations of the techniques employed;
D. Conduct advanced analysis of complex data and develop real-world
applications of Data Mining and Big Data Analytics. These analysis
should evidence some originality and meet a combination of societal,
user, business and customer needs as appropriate;
E. Work as a member of a development team recognising the different
roles within a team and different ways of organising teams. Evaluate
the effectiveness of own and team performance.

Method of teaching and learning

Students will be expected to attend two hours of formal lectures as well as to participate in two hours of supervised practical (tutorial / lab) classes in a typical week. Students will be asked to devote Seven hours of unsupervised time for reflection and consideration of lecture material and will be required to research and read widely on the subject, and where possible use their personal experiences from work placements. Students will be encouraged to explore the challenges and opportunities of analytics delivery within a business.