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.
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;
C Show expertise in the use of state-of-the art software tools for the implementation of solutions to Data Mining and Big Data Analytics;
D Conduct an advanced analysis of complex data and develop real world applications of Data Mining and Big Data Analytics.
E. Work as a member of a development team recognising the different roles within a team and different ways of organising teams.
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.