MSc Data Science

The need for computational power and data storage continues to drive demand for more highly capable systems. Highly data intensive applications demand fast access to terabytes, petabytes, even exabytes of storage; processor intensive applications demand access to various types of processors in various configurations. Such applications are increasingly being developed in both scientific and industrial contexts and need to be variously scalable and supportable for large numbers of geographically distributed users. This module will provide insights into how Cloud Computing attempts to meet the varying needs of such applications.

The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools.

A key aspect of business operations today, across sectors almost, has to do with gathering the right type of data and storing it in a way that it can be readily available to the right person at the right time. This course looks into the techniques that allow us nowadays to define and operate on large volumes of data as and when it is created. This paves the way for making more intelligent uses of data, whether this has to do with correctness (reliability and consistency) or informing more strategic decisions of the business so it can better prepare itself for the future.

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.

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 Amazon, Netflix and Google.

This module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted. Good practice in the responsible and effective use of generative AI for research development and academic writing will be demonstrated and critically discussed. This module serves as an important initial module towards planning and executing the dissertation project. It also provides a platform to facilitate team building skills and make the student aware of the challenges of working effectively with other people embracing equality, diversity and inclusion.

In today¿s world where companies can amass more and more fine-grained data, it is crucial for a business to understand how this data can be used to effectively drive the business forward. Business Analytics is a set of methods and tools that can transform data into useful insights for decision-making. For example machine learning algorithms can be used to discover interesting patterns in the current market data or to predict customer behaviour (e.g. customer churn) from past data.

Machine Learning for Data Science incorporates a wide range of machine learning algorithms and data mining techniques, which can be applied to real-world problems and datasets with various characteristics to generate new insights and understanding. Through treatment of the principles and fundamental requirements for machine learning, example applications, and related exercises, this module will offer coverage of a range of contemporarily important and emergent machine learning algorithms. The module will provide for the means to critically evaluate, extend, and apply, appropriate techniques to datasets exemplifying specific characteristics in order to derive suitable and defensible results.

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.

Machine learning and deep learning have emerged as core areas within computer science and artificial intelligence, drawing on methods from statistics, applied mathematics, pattern recognition, and neural network computation. This module provides a comprehensive introduction to the theory and application of advanced machine learning and deep learning techniques.The module explores how these methods are applied across a wide range of domains, including natural language processing, large language models, medical imaging, healthcare, audio and speech processing, computer vision, multimodal large language models, large multimodal models and financial technologies (fintech). Students will gain an understanding of how modern AI systems are designed to process and analyse diverse data types such as images, video, text, and audio.The deep learning algorithms covered in this module are widely used in industry, from AI start-ups to major technology companies such as Google, Meta, Microsoft, Nvidia, Amazon, and Tesla. The module combines theoretical foundations with practical implementation, enabling students to develop, evaluate, and apply machine learning models to real-world problems.

The dissertation consists of a substantial written report. This report is based on a major piece of work that involves applying material encountered in the taught component of the degree, and extending that knowledge with the student's contribution, under the guidance of a supervisor. The dissertation usually involves a substantial literature survey on a specific topic, followed by the identification of a problem to tackle, and thereafter the development of a technical solution, and experimental or theoretical evaluation of the achievement.