MSc — 2024 entry Data Science

Security is probably the greatest challenge for computer and information system in the near future. Many users have lost data due to viruses, both on home and business computers. Most of us have seen a range of emails messages attempting different kinds of fraud. Vulnerabilities are everywhere. Some are obvious or well-known; others are obscure and harder to spot. Security is not limited to secrecy and confidentiality, but also involves problems like integrity, availability, and effectiveness of information. Moreover, security issues can potentially affect all of us, from innocent home users to companies and even governments. Security is not just a technical problem but needs to be embedded throughout an organisation to be effective. As such good security solutions build on a complete understanding of the values at stake, and the supporting business processes and requirements. This includes people as well as information systems and physical resources. Consequently, raising security awareness and embedding security within roles and policies is as important, if not more, as secure software. In short, secure solutions can only be implemented with both good technical skills and a good understanding of cultures and people skills. This module aims at raising the awareness for the wide range of security issues present in today’s connected world and the managerial and organisational challenges a business must face when building a secure solution.

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Key to modern business operation is Risk Management, and a modern entrant on any Risk Register maintained by the executive of an organisation is the risk posed from falling victim to cybercrime and civil litigation resulting from, for example, data breaches. In order to understand how best to shape and evolve an organisation’s response to the cyber security threat, the module will cover what the more detailed operations in cyber security involve. The module will introduce the key disciplines that we are likely to encounter in the real-world.  It will provide an industry perspective on the common practices, latest innovations, technologies and the problems and concerns associated with information security. The module will show that cyber security is not just technical subject, but is a truly multidisciplinary function. The module looks broadly at the implications of regulations on the cyber crime landscape. It also brings together the issues of cyber from a high-level perspective. It will also equip students with an awareness of what is very current out there in terms of leading-edge applications of information security. The module uniquely accesses leading international speakers in their field, draws on the significant expertise of leading industrialists and through shaping the module into distinct learning blocks we are also able to ensure that our students will be directed to key industry reports, regulations and best practice guidance that shapes current industry thinking in information security for business and government.  

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

Security is probably the greatest challenge for computer and information system in the near future. Many users have lost data due to viruses, both on home and business computers. Most of us have seen a range of emails messages attempting different kinds of fraud. Vulnerabilities are everywhere. Some are obvious or well-known; others are obscure and harder to spot. Security is not limited to secrecy and confidentiality, but also involves problems like integrity, availability, and effectiveness of information. Moreover, security issues can potentially affect all of us, from innocent home users to companies and even governments. Security is not just a technical problem but needs to be embedded throughout an organisation to be effective. As such good security solutions build on a complete understanding of the values at stake, and the supporting business processes and requirements. This includes people as well as information systems and physical resources. Consequently, raising security awareness and embedding security within roles and policies is as important, if not more, as secure software. In short, secure solutions can only be implemented with both good technical skills and a good understanding of cultures and people skills. This module aims at raising the awareness for the wide range of security issues present in today’s connected world and the managerial and organisational challenges a business must face when building a secure solution.

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Key to modern business operation is Risk Management, and a modern entrant on any Risk Register maintained by the executive of an organisation is the risk posed from falling victim to cybercrime and civil litigation resulting from, for example, data breaches. In order to understand how best to shape and evolve an organisation’s response to the cyber security threat, the module will cover what the more detailed operations in cyber security involve. The module will introduce the key disciplines that we are likely to encounter in the real-world.  It will provide an industry perspective on the common practices, latest innovations, technologies and the problems and concerns associated with information security. The module will show that cyber security is not just technical subject, but is a truly multidisciplinary function. The module looks broadly at the implications of regulations on the cyber crime landscape. It also brings together the issues of cyber from a high-level perspective. It will also equip students with an awareness of what is very current out there in terms of leading-edge applications of information security. The module uniquely accesses leading international speakers in their field, draws on the significant expertise of leading industrialists and through shaping the module into distinct learning blocks we are also able to ensure that our students will be directed to key industry reports, regulations and best practice guidance that shapes current industry thinking in information security for business and government.  

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.  

Security is probably the greatest challenge for computer and information system in the near future. Many users have lost data due to viruses, both on home and business computers. Most of us have seen a range of emails messages attempting different kinds of fraud. Vulnerabilities are everywhere. Some are obvious or well-known; others are obscure and harder to spot. Security is not limited to secrecy and confidentiality, but also involves problems like integrity, availability, and effectiveness of information. Moreover, security issues can potentially affect all of us, from innocent home users to companies and even governments. Security is not just a technical problem but needs to be embedded throughout an organisation to be effective. As such good security solutions build on a complete understanding of the values at stake, and the supporting business processes and requirements. This includes people as well as information systems and physical resources. Consequently, raising security awareness and embedding security within roles and policies is as important, if not more, as secure software. In short, secure solutions can only be implemented with both good technical skills and a good understanding of cultures and people skills. This module aims at raising the awareness for the wide range of security issues present in today’s connected world and the managerial and organisational challenges a business must face when building a secure solution.

This module gives an introductory yet up-to-date description of the fundamental technologies of computational Intelligence, including evolutionary computation, neural computing and their applications. Main streams of evolutionary algorithms and meta-heuristics, including genetic algorithms, evolution strategies, genetic programming, particle swarm optimization will be taught. Basic neural network models and learning algorithms will be introduced. Interactions between evolution and learning, real-world applications to optimization and robotics, and recent advances will also be discussed. Good skill in Python programming, good knowledge in mathematics (calculus) are required.

Key to modern business operation is Risk Management, and a modern entrant on any Risk Register maintained by the executive of an organisation is the risk posed from falling victim to cybercrime and civil litigation resulting from, for example, data breaches. In order to understand how best to shape and evolve an organisation’s response to the cyber security threat, the module will cover what the more detailed operations in cyber security involve. The module will introduce the key disciplines that we are likely to encounter in the real-world.  It will provide an industry perspective on the common practices, latest innovations, technologies and the problems and concerns associated with information security. The module will show that cyber security is not just technical subject, but is a truly multidisciplinary function. The module looks broadly at the implications of regulations on the cyber crime landscape. It also brings together the issues of cyber from a high-level perspective. It will also equip students with an awareness of what is very current out there in terms of leading-edge applications of information security. The module uniquely accesses leading international speakers in their field, draws on the significant expertise of leading industrialists and through shaping the module into distinct learning blocks we are also able to ensure that our students will be directed to key industry reports, regulations and best practice guidance that shapes current industry thinking in information security for business and government.  

This module introduces students to some of the basic ideas and concepts that underlie the development of artificially intelligent machine systems. Teaches core AI materials for problem solving (search, logic, probabilistic methods, Perceptrons as the building block for ANNs) Focuses on core understanding and problem solving: suitable tools/methods for a problem using problem classes Provides a clear understanding of neural networks, back-propagation, RBFs, ANN learning and optimisation Provides a clear understanding of intelligent agents via search methods and introducing cost functions Provides a clear understanding of Bayes’ Rule, conditional probability and uncertainty reasoning Provides a clear understanding of knowledge capture, symbolic knowledge representation and logical reasoning from antiquity to Boule to first order predicate rules and representations. Provides opportunity to implement concepts during coursework.

This module will demonstrate fundamental concepts from the field of Natural Language Processing (NLP) and Computational Linguistics. It will also discuss some of the latest advances in NLP and Generative Artificial Intelligence with a focus on Language Models like BERT, T5, and GPT, and get student up to speed with current research. It will provide the necessary skills to enable students to build computational models for solving a range of problems, such as text classification, sequence classification, machine translation and building conversation agents. The students will learn how to build NLP pipelines for preparing training data and choosing appropriate algorithms and techniques to build such models. The module also focuses on aspects of ethical and trustworthy artificial intelligence with discussion on rigorous model evaluation and ethical considerations for computational modeling. Although traditional linguistic approaches will be mentioned, majority emphasis will be put on the state-of-the-art Deep Learning algorithms and Transfer Learning methods for building efficient and trustworthy NLP solutions. 

This module examines the architectural/design needs and challenges encountered when developing and deploying secure, resilient and scalable web applications using the latest technology. It also provides an introduction to approaches used in modern Internet-scale web applications, covering technologies used in and/or developed by familiar companies such as Twitter, Amazon, Netflix and Google.