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Opleiding: MSc Data and Knowledge Engineering Degree

Learning purpose

Data and knowledge engineering helps businesses achieve competitive advantage through high-end business intelligence. The Data and Knowledge Engineering masters enables you to understand, apply and assess knowledge and skills required for the development and application of data warehousing and data mining systems. These systems underpin many enterprise resource planning (ERP) and customer relationship management (CRM) systems, which are increasingly becoming indispensible in the planning and execution of competitive strategies in industry and commerce.

 

Programme

There are four taught modules, which are followed by an independent project. The course providesyou with a deeper knowledge and advanced skills and understanding of data warehousing, data mining, data security and privacy, knowledge representation and research methods.

 

Data Management for Decision Support (30 Credits) - Compulsory

Data management systems are now the central software of most industrial data processing applications and the driving force behind Business Information Technology and E-Commerce. This module covers the concepts and theories of managing industrial data such as implementation of large volume data storage, preserving data quality, data preparation for dimensional modelling. The module also explores technologies and algorithms for handling a large amount of data, i.e., data cleansing, data segmentation and data transformation, to discover knowledge from data warehouse. This module presents state of the art technologies that support decision-making and provide problem solving capabilities to managers in industry. The module is designed to give an introduction into the theory of decision-making and management and some of the most widely used contemporary methods enabling modern decision support systems. In particular, the module explores theories in the domain of management decision making, for example, linear models, utility and decision-making under uncertainty. The module provides students with an opportunity to examine how data management techniques can be applied in business information systems to enhance the decision making power of managers. The module also equips students with sufficient understanding of the current theory of decision making and its applications that address modern and future business needs.

Knowledge Discovery (30 Credits) - Compulsory

This module presents state of the art technologies that are quickly entering business applications to discover new knowledge and support decision-making and operations in organisations. Commercial enterprises are investing into research and development of these systems to gain technological advantage over their competitors and employ a great range of techniques from standard statistical methods to more recently emerged applications of data mining and knowledge discovery in databases KDD , artificial intelligence AI and operations research OR . The module is designed to give an introduction into the theory of decision-making, and how information and knowledge can facilitate the decision-making process. Some of the most important, popular and state of the art methods of knowledge discovery are presented. In particular, the module dedicates several units to the theories of linear models, neural networks for pattern recognition and data analysis. Some of the knowledge representation and management techniques emerged form AI, such as rule-based and case-based expert systems, will be presented in the rest of the module as well as some optimisation techniques, such as genetic algorithms. The module will equip students with sufficient understanding of the current theory of knowledge discovery and decision making. By the end of the course, students should be able to use a variety of data-mining tools to address modern and future business problem. Students will be encouraged to make the comparison between human intelligence and artificial intelligence.

Multi-Channel E-Commerce (30 Credits) - Compulsory

An adaptive website is capable of presenting a different personalised interface to each user, based on their browsing history and other characteristics. This module is designed to develop students understanding of the use of multichannel adaptive websites to facilitate e-commerce. It equips students with knowledge and skills required to analyse, design and implement an adaptive website, using state-of-the-art technologies such as semantic web and XML, to monitor the characteristics of the user and extrapolate future patterns. The module will also cover the characteristics of point to point virtual systems and the scheduling issues and interleaving technology that results from an e-commerce architecture using multiple web servers. A number of in-depth case studies are provided to the student to advance students understanding of the practical application of these technologies.

Research Methods in Knowledge Engineering and Discovery (30 Credits) - Compulsory

This module will show students how to explore research topics and familiarise them with specific active research topics. A range of research topics and methods will be explored and each should provide a unique view on the overall problem of researching novel topics in Knowledge Engineering. Qualitative and quantitative methods will be discussed in depth. Each topic will contribute its own Set of problems - each topic will have its own problems that are currently active and some that are solved. The students will become familiar with them. Research method - each topic will have its own research method or methods. Some of these methods will be explored during the sessions on that topic. Body of literature - each topic will have its own body of literature. The students will become familiar with journals, websites, authors, groups and seminal works. Current state of the field - each topic will have particularly active sub-areas, recently solved problems, and foundations. The students will become familiar with this state. There will almost certainly be some overlap between topics in some or all of these areas. In addition to these individual areas, the students will develop a Range of literature search methods - each area will require the student to look for literature and participate in discussion both in class and via the internet. Understanding of the range of the problems involved in Knowledge Engineering and AI - nine topics will be presented; combined with other modules the students will develop an understanding for the range of problems. Understanding of the relationships between areas - each topic will have some relation to the others and to the other modules. These relationships will be explored.

 

 

Entry Requirements

We welcome applications from graduates with a good honours degree, or equivalent qualification, in an appropriate subject. We also consider candidates with other relevant qualifications and individuals with a minimum of three years' work experience. Those without formal qualifications need to demonstrate relevant work experience and the ability to study at postgraduate level.

You must have competence in English language and we normally require Grade C GCSE or an equivalent qualification. The most common English Language requirements for international students are IELTS 6.5 or TOEFL (paper based) 575 or TOEFL (internet based) 90 with specified minimum scores for each component.

 

Professional perspective and employment market

What can you do with a Computing and IT degree?
Data and Knowledge management is a growing and buoyant employment sector and the industry is expanding rapidly, providing a wide range of opportunities for graduates properly equipped with a broad range of managerial, professional and technical knowledge in data warehousing, data mining and knowledge engineering.

 

Where do our graduates go?
Based on data from the respondents of the 2007 Destination of Leavers in Higher Education survey, six months after graduating

• 69% of graduates from the BIS departmentwere in employment
• 25% went on to do further study
• 3% were assumed unemployed
• 3% were doing something else, such as travelling.

 

 

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