This module aims to:
- develop a critical awareness of the range of tools being marketed under the label "Decision Support" or more generally "Business Intelligence" (BI);
- provide an understanding of the key algorithms and techniques which are embodied in Business Intelligence solutions.
After completing this module participants will be able to:
- understand the way in which classical statistical techniques are applied in modern BI solutions;
- discuss the potential application of BI tools to various types of business problem-solving and appreciate their limitations;
- describe the range of computer based approaches to representation and reasoning with formal and quantitative knowledge;
- understand the role of end users and analysts in BI, and the important differences between decision support and decision-making.
Indicative topics:
- Modelling the mind: deduction, induction, machine learning and neural networks;
- Knowledge-based tools: expert systems, case-based reasoning, DSS;
- Quantitative methods for data analysis and knowledge extraction: classification and regression, clustering, association rule, Bayesian approaches, belief networks;
- Modelling, simulation, optimisation and uncertainty;
- BI applications: data mining, knowledge management, decision analysis, text mining, etc.