Our MSc Data Science and Artificial Intelligence will give you the knowledge of methods and processes that will enable you to analyse, devise, and deploy data science and artificial intelligence solutions for real-world problems. In particular, this degree emphasises different facets of exploratory/confirmatory data science techniques and artificial intelligence algorithms in different contexts, ranging from digital health to fraud detection. You will learn about recent advances in the fields of data science and artificial intelligence, including state-of-the-art tools to perform analytics and experiments to enable data-driven decision making and automation.
You will graduate with skills in methodological thinking, research disposition, and communication, in addition to the theoretical and practical skills within data science and artificial intelligence.
A Bachelors Honours degree with 2:2 in a required subject, or equivalent.
Required subject: Computing, Technology, Maths, Physics, Engineering, Data Sciences or Data Analytics
International entry requirements
If English is not your first language, you will need to provide evidence that you understand English to a satisfactory level. English language requirements for this course are normally:
IELTS (Academic) 6.0 with minimum 5.5 in each component, or equivalent.
Research Methods and Professional Issues: This unit will provide an overview of different research methods used to address clinical research questions. It will cover aspects of research design and how they apply to the question being asked whether the approach is quantitative, qualitative or mixed methods.
Search and optimisation: These techniques are employed in a vast number of areas, including medicine, defence, transportation, aerospace, and finance. Whether the goal is to improve the performance of a new drug, aircraft, traffic signal controllers, or investment decisions, stochastic optimisation algorithms can be employed by researchers and practitioners to design optimal, diverse, and pertinent solutions to many real-world problems. This unit introduces classical approaches to search and optimisation before moving onto the state-of-the-art in computational intelligence. The unit entails a strong empirical element where Computational Intelligence models will be implemented and evaluated.
Data Processing and Analytics: The unit aims to develop student knowledge and skills in the evolving areas of Data Modelling, Data Analytics and Big Data Analytics. Students develop an understanding of data design, implementation and use of data-driven systems. Moreover, they learn how to model data and big data, discover knowledge within the data and deal with the dimensionality of the data. Overall, they develop critical understanding of the methodologies and techniques to cope with Data and Big Data, i.e. data of high volume, high velocity and high variety, utilising the appropriate platform (e.g. Hadoop, Storm, Spark, MongoDB).
Artificial Intelligence: The aim of this unit is to provide you with an introduction to the principles and techniques employed within the greater field and sub-fields of Artificial Intelligence (AI), and the skills and knowledge required to employ AI techniques to solve real-world and synthetic problems. We will approach AI from a computer-science perspective, with focus given to the challenges faced within the field, nature inspired algorithms, and their applications to complex real-world problems.
Individual Masters Project: You will develop an understanding of the characteristics and implications inherent in the solution of a complex, real-world problem within the context of a substantial, independently-conducted piece of work.
Neuronal Analysis: This unit focuses on the study and application of data analysis techniques in neuroscience. Such algorithms are either adapted or specifically designed for analysing neuronal activity in physiological and pathological brain states. The mathematical modelling of basic neuronal functions over the last century, inspired the development of sophisticated parallel data processors that imitate biological neurons and networks to a certain degree. A successful example is the widely used family of deep learning-based approaches. In parallel, methods from e.g., computational statistics, dynamical systems, partial differential equations and more recently machine learning, feedback to the neuroscience field; and provided valuable insights in the understanding of the nervous system. This unit discusses state-of-the-art analytical tools for identifying normal and altered behaviour of neuronal activity at multiple spatiotemporal scales, their applications and current limitations.
Blockchain and Digital Futures: The objective of this unit is to develop your skills and knowledge about the Blockchain technology and its usage. The material, lectures and seminars includes defining the Blockchain technology, its business aspect, issues, objectives, and challenges, covering Blockchain horizontal and vertical scaling, key basics of cryptography required for understanding the Blockchain technology concepts, different cryptocurrencies, and their issues, challenges, and networks. The unit also covers a few data analysis-based Blockchain technology scenarios and case studies. The aim is to develop related skills and an understanding of critically evaluating the key issues, challenges, and existing solutions.
Smart Systems: Artificial Intelligence is being embedded in various systems and tools to achieve better decision making and more autonomy. The unit draws on a large spectrum of smart systems technologies. It covers basic as well as advanced topics with the goal of providing an overall introduction into these technologies. Positioned at the cross of many disciplines, this unit introduces various facets of smart systems technology and illustrates how they apply to different environments. A selected set of innovative applications will be discussed to provide a practical insight into such technology covering in particular the underlying modelling, perception, reasoning and learning techniques.
Computer Vision: The field of Computer Vision has been developing rapidly over the last decade and became the cornerstone of many recent innovations across a number of domains and application areas, including state-of-the-art image classification (e.g. for use in search engines), object detection (e.g. in autonomous vehicles), semantic segmentation (e.g. in identification of tumours in x-ray images), biometrics (e.g. face verification) or automatic annotation of medical images, to name a few. There is a shortage of skilled individuals with deep understanding of the underlying principles and able to confidently apply them in order to solve real-world problems. This unit focuses on a number of Computer Vision techniques, ranging from traditional approaches established over many decades of research, to the latest state-of-the-art developments, together with appropriate tools. The unit entails a strong empirical element where Computer Vision models will be built and evaluated.
Upon completion of this course, you will possess the practical skills and theoretical knowledge necessary to enter fields within data science and artificial intelligence.
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