Course Details
Course Information Package
Course Unit Title | DECISION SUPPORT AND KNOWLEDGE-BASED SYSTEMS | ||||||||
Course Unit Code | ACSC416 | ||||||||
Course Unit Details | |||||||||
Number of ECTS credits allocated | 5 | ||||||||
Learning Outcomes of the course unit | By the end of the course, the students should be able to:
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Mode of Delivery | Face-to-face | ||||||||
Prerequisites | NONE | Co-requisites | NONE | ||||||
Recommended optional program components | NONE | ||||||||
Course Contents | Decision Support Systems: What are decision support systems. Ingredients of a DSS. Data and model management. DSS knowledge base. The concept of knowledge. User interfaces. The DSS user. Categories and classes of DSS systems. Decisions: What are decisions. Why are decisions so hard. How can DSS help in making decisions. Rational decision-making. Bounded rationality. The process of choice. Cognitive processes. Biases and heuristics in decision-making. Effectiveness and efficiency. Modelling Decision Processes: Defining the problem and its structure. Decision Models. Types of probability. Techniques for forecasting probabilities. Calibration and sensitivity. Data Warehouses: Data warehousing concepts. Data warehousing benefits and problems. Stores, warehouses, and marts. The data warehouse architecture. Tools and technologies. The metadata. Data warehouse design. Design methodology. Criteria for assessing dimensionality. Analysis and design tools. Data warehouse implementation. Data Mining: What is data mining. Online analytical processing and its purpose. Key features of OLAP applications. Multidimensional OLAP. Relational OLAP. Data mining techniques, technologies and applications. Market basket analysis. Limitations and challenges to data mining. The relationship between data mining and data warehousing. Data Visualisation: What is data visualisation. Human visual perception and data visualisation. Geographical information systems. Data visualisation applications. Data visualisation technologies. | ||||||||
Recommended and/or required reading: | |||||||||
Textbooks |
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References |
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Planned learning activities and teaching methods | The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of the core concepts and techniques. Lectures also include the discussion and analysis of case studies to provide students with practical understanding of the application of decision support technologies. Furthermore, a lot of the work is done through homework and private study by the exploration and experimentation with software tools for decision support, such as the Weka data mining platform. All lecture notes and other material is available to students through the course homepage. | ||||||||
Assessment methods and criteria |
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Language of instruction | English | ||||||||
Work placement(s) | NO |