15 Credits SPRING

Aims/Description: As the volume of and types of information collected and stored in databases grows, there is a growing need to gain new insights into the data by identifying important patterns and trends, and summarising the findings to inform decision making. Such insights can provide huge economic value and competitive advantages. Data Mining is the process by which this is done. This module will examine fundamental algorithms for clustering and classifying data as well as data visualisation strategies and data mining applications. Students will be introduced to key themes in data mining, including types of data mining problem (e.g. classification, clustering, rule mining), common algorithms used in machine learning (e.g. SVM, decision trees, k-means), feature selection and evaluation issues (e.g. measures and standardised benchmarks). It will also explore Data Visualisation (and visual analytics), the graphical representation of information that provides a qualitative understanding of the information on which decisions can be based. The last part of the module will present different applications of data mining to improve businesses (e.g., opinion mining) and user experience (e.g., recommender systems). Case studies will be used throughout the module to highlight the use of data mining methods and visualisation for tackling real-world problems. Students will gain practical hands-on experience through the use of widely used software tools (e.g. WEKA for data mining and Tableau Public for visualisation).

Staff Contact: Prof Val Gillet
Teaching Methods: Lectures, Problem solving, Laboratory work, Independent Study
Assessment: Course work
WebCT resources are available for this module

Information on the department responsible for this unit (Information School):

Departmental Home Page
Teaching timetable


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