This module aims to provide students with an understanding of how different kinds of phenomena are represented as digital information. Its objectives are to give students an appreciation of the role of software in rendering and manipulating digital representations, and an introduction to the skills and techniques of abstract representation (modelling) of social and economic phenomena.

What is a representation? the represented world, the representing world and the mapping between the represented and representing world; intrinsic versus extrinsic mappings; Representing information in various forms of media (images, graphics, video, audio and text); characteristics of multimedia data; hypertext and hypermedia; document content and structure; content model; semantic structure; metadata and metatags; modelling media objects; modelling correlations among media objects; simulation versus animation; What is a model? model criteria: mapping criterion, reduction criterion, pragmatic criterion; models versus real systems; abstraction and similarity; iconic, analogic and symbolic models; static and dynamic models; descriptive and prescriptive models; metaphor as a special type of model; purposes of models; Analyzing social, biological and business phenomena, in order to design and construct models of those phenomena, using spreadsheets and databases; Models in software development; use of descriptive and prescriptive models; risks associated with model usage; formal approach to building models; problem conceptualization; collection and examination of data; model structure, content and layout; development and use of macros; model validation and documentation; developing model templates.

On successful completion of this module, students will be able to: 1. Describe various forms of representations and terminology; 2. Describe models and various types of models and their purpose; 3. Present models of computerized data indicating its storage and representation; 4. Recognise the properties of particular artefacts; 5. Identify whether particular artefacts are models; 6. Illustrate why particular artefacts are/are not models; 7. Analyse types of models; 8. Design a simple model; 9. Use specific software to construct the visualisation of a simple model; 10. Use spreadsheets to construct models for simple and relevant problems; 11. Use databases to construct models for simple and relevant problems.

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The module is taught as a combination of lectures and labs where students are introduced to methods and models for algorithmic problem solving during lectures and the labs will allow students to apply and perfect the practical knowledge generated through lecture-based learning. Students will be evaluated for practical competency by continual assessment of lab-based activities that will cover the various methods and models introduced.