BSc Data Science

COM1011: Fundamentals of Machine Learning

Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.

Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.

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ECM1400: Programming

We use computers in almost all aspects of our daily lives and throughout science, so it is easy to take them for granted. However, in order that we can use computers to solve new problems and create new things, we have to be able to program them. This module introduces you to programming and problem solving with a computer.  You will learn how to formulate an algorithm to solve a problem, and you will acquire the skills to write, test and debug programs.

This module is an introductory course in computer programming and will introduce you to the fundamental concepts of computer algorithms and programming, with a strong emphasis on practical implementation. You will also learn how to apply analytical and problem-solving skills to the design and implementation of small applications.

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ECM1407: Social and Professional Issues of the Information Age

The module aims to provide you with the tools to reflect upon your role in the interface between digital technologies and society and on the moral and ethical use of information and information systems. By taking this module, you will become aware of your legal responsibilities and rights as an IT professional and as a user of digital technologies. The module will cover ethical theories, computer law and professional codes of conduct, and will address the ways in which broader areas of law (e.g. defamation, contracts, privacy and freedom of information legislation) impact upon technology users and IT professionals.

This module will introduce you to the law regulating the use of information and digital technology. It will enhance your awareness and critical thinking skills regarding the social impact of information technology, and help you relate professional codes of conduct to ethical theories.

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ECM1410: Object-Oriented Programming

This module will introduce you to object-oriented problem-solving methods and provide you with object-oriented (OO) techniques for the analysis, design and implementation of solutions. We will introduce you to these concepts, and you will develop skills with a new programming language. By the end of this module, you will be able to apply these skills to design and implement small applications.

The module aims to provide you with a thorough grounding in the fundamentals of object-oriented design concepts, alongside the fundamentals of the Java programming language, and general object-oriented design concepts. It will also introduce you to widely used components of the unified modelling language (UML), teach you how to interpret and implement a Java program from these higher-level designs, along with the pair programming approach used in industry.

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ECM1413: Computers and the Internet

This module is designed to equip you with the foundational information you need to understand and work in business and technical fields requiring the use of computers and networking technologies. Computing technology has a diversity of applications, so this module is suitable both for computer science students and for those pursuing other study disciplines. On this module, you will acquire foundational knowledge of computer systems (operating system and computer architecture) and computer networks.

By the end of the module, you should be well placed to make use of an extensive range of hardware and software technologies. In addition, you will have gained the knowledge and skills to enable you to analyse existing computer- and internet-based information systems.

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ECM1414: Data Structures and Algorithms

According to an old formula, Algorithms + Data Structures = Programs. This remains as true today as when it was originally formulated by Niklaus Wirth in 1976, and encapsulates the truism that all computation consists of the manipulation of data by means of systematic procedures. But data comes in many different forms (e.g., numerical, alphabetical, graphical) and only by knowing how it is structured can we specify the procedures – algorithms – for manipulating it to produce desired outcomes. Thus, the study of data structures and algorithms constitutes an integrated topic, which forms the subject matter of this module. You will be introduced to some of the key concepts in the area, with plenty of examples to illustrate them, and you will be given a chance to demonstrate your understanding by undertaking exercises. This module builds on the programming knowledge you have already acquired from ECM1400 Programming and will make use of mathematical tools introduced in ECM1415 (Discrete Mathematics for Computer Science) to enable data structures and algorithms to be described precisely.

Prerequisite module: ECM1400, ECM1415 or equivalent.

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ECM1415: Discrete Mathematics for Computer Science

Discrete mathematics is concerned with quantities which vary discretely, and because of that has an important role in Computer Science, in which discrete structures such as sets, graphs, lists, and trees play a fundamental role, and the underlying forms of reasoning are based on propositional and predicate logic rather than on calculus and mathematical analysis, with an emphasis on counting rather than measuring, e.g. enumerating permutations and combinations of objects satisfying specified conditions. This module will provide a thorough grounding in the fundamental structures and methods of discrete mathematics that are required for computer science.

The aim of this module is to provide you with the basic concepts and tools developed in discrete mathematics disciplines but needed for the study of computer science. As such, it forms an essential part of a rounded education of a computer scientist or computer expert whose work includes computer-based data manipulations.

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ECM1416: Computational Mathematics

Computer science draws from a wide range of essential mathematical techniques. This module will provide a solid foundation on the required mathematical tools and how to use them in solving computer science problems. This module will introduce linear algebra and vector spaces, statistics and probabilities and numerical optimization.  In the course of this module, you will learn to apply theoretical knowledge in concrete programming tasks.  This module complements previous mathematics module and is essential for all engaged in a Computer Science program.

In this module we aim to provide you with a foundation in the essential mathematical tools used in advanced computer science topics. We will teach you how to use vector and matrices, statistics and probabilities and numerical optimization methods and implement them in computer programs.

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COM2011: Machine Learning and Data Science

This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.

This module is suitable for students with sufficient preparation in Mathematics and Programming.

This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.

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COM2020: Team Project

This module gives you the opportunity to work collaboratively on a substantial practical problem, which you will solve from a computational and data-based perspective. Teams will apply technical skills in software development and data analysis while managing project planning, teamwork, and communication. During the module you will design and develop a solution that balances innovation with feasibility, and develop a prototype that you demonstrate tackles the project according to specific measures of success. You will develop the professional skills required to succeed in technology and data-driven industries.

The aim of this module is to equip you with the necessary practical and theoretical skills to enable you to develop and implement a computational and data-driven solution to a given problem. Early in the module you will be presented with a realistic problem, and you will be asked to work within a team to propose, develop, and implement a solution to the problem. You will learn how to apply a range of evaluation measures to evaluate the success of your team’s solution. Throughout the module you will learn about and deploy teamwork skills to ensure the success of your project.

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ECM2414: Software Development

The module will introduce you to software design and development concepts and methods, alongside intermediate and advanced constructs and concepts in the Java programming language, and the programming paradigms these relate to. This includes generic programming (and Java generics), concurrent programming (via Java threads), design patterns, networked programs and nested inner classes. We will also cover widespread tools in software development, including version control and unit testing.

This module will introduce you to methods for the rigorous testing and assessment of software, and prepare you for complex programming tasks in a specific object-oriented programming language, including advanced concepts and syntax, and the use of multiple programs in parallel.

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ECM2419: Database Theory and Design

This module will give you an insight into the theoretical and technical issues underlying current and future database management systems. You will acquire practical and theoretical competence in database modelling and design, as well as gaining familiarity with modern state-of-the-art database technology.

Prerequisite module: ECM1400, ECM1410, ECM1413 or equivalent.

The intention of the module is to equip you with the theoretical and practical knowledge needed to design, develop and manage database systems using modern database management systems. You will get hands-on experience on a selected database management system that is currently in commercial use. By the end of the module you will be competent with the methods for designing, developing and managing database systems and their associated forms-based applications.

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MTH2006: Statistical Modelling and Inference

Statistical modelling lies at the heart of modern data analysis, helping us to describe and predict the real world. Statistical inference is the way that we use data and other information to learn about and apply statistical models. In this module, you will learn the theory underpinning modern statistical methods such as fitting normal linear models, evaluating how well they fit the data and taking inferences from it. You will apply the theory using statistical software such as R to analyse and draw conclusions from a range of real-world data sets. Topics covered in the module range from estimators, confidence intervals, design of experiments and hypothesis testing to statistical modelling, regression, inference and comparison of models. Skills developed in the module are taken further in modules such as MTH3012 Advanced Statistical Modelling.

This module aims to develop understanding and competence in statistical modelling by introducing you to the Normal linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.

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SPA2009: Data Science in Society

This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.

Suitable for non-specialists and interdisciplinary pathways.

This module aims to:

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COM2014: Computational Intelligence

Computational intelligence is the science of computational systems that are able to perform specific tasks, adapting to particular data. The module will equip you to design and use computational intelligence to solve a variety of problems such as planning, scheduling, optimisation, using a variety of techniques including biologically inspired computational, fuzzy logic, agent-based models and simulation.

Pre-requisite Modules: COM2013 (Data Science Group Project 2); ECM1400; MTH1004

The aim of this module is to introduce and give you practice in some of the main areas of computational intelligence that can be used to solve problems arising in data science. It aims to give you and understanding of the theoretical basis of these methods and their relation to other artificial intelligence techniques. Specifically, it will introduce classical “crisp” logic and knowledge representation before proceeding to fuzzy logic to cope with uncertain and vague processes. Searching and optimisation arise in many contexts and this module aims to introduce you deterministic and stochastic optimisation methods, particularly evolutionary optimisation.

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ECM2423: Artificial Intelligence and Applications

Artificial Intelligence is the science of getting computers to do things which, when done by humans, involve the exercise of intelligence. It has been an important strand of Computer Science throughout the lifetime of that discipline, and has exerted a significant influence on other areas of Computer Science as well as on practical applications. This module will provide you with a broad overview of Artificial Intelligence, as well as a more detailed understanding, both practical and theoretical, of selected topics within this area. This module is suitable for any student who has a basic knowledge of computer programming, as well as linear algebra, discrete mathematics, and probability theory.

Pre-requisites: ECM1415 and ECM2418

In this module we aim to provide you with a general introduction to some of the main topics within the broad field of Artificial Intelligence, beginning with an overview of the history and philosophy of AI, then proceeding to a more detailed examination of a range of specific sub-areas, including logic and knowledge representation, searching algorithms, machine learning, and natural language processing.

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ECM2427: Outside the box: Computer Science Research and Applications

This module gives you a chance to explore the breadth and depth of Computer Science beyond the core technical content of the main syllabus, and to investigate current research in Computer Science and how it is used to solve problems in other areas. It will explore some of the frontiers of research in the department and, through lectures by specialists in other fields, will introduce you to some of the uses of Computer Science methods in business, the sciences, social sciences and humanities.

This module aims to introduce students to current Computer Science beyond the confines of the main syllabus. On one side, it will introduce you to some of the research into new ideas in Computer Science, and on the other, it will explore some applications where Computer Science is essential. You will learn about the nature and purpose of research, some current research problems, the methods employed to tackle them, and how the results are evaluated. You will also learn about some of the ways existing Computer Science techniques and technologies are applied to solve problems outside Computer Science, particularly large-scale computing applications.

You will demonstrate what you have learnt by producing an in-depth review on one of the topics covered by the seminars, and in groups, you will also find out about a current topic of Computer Science and technology and make a presentation on it.

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ECM3419: Industrial Placement

This module will provide you with extensive practical work experience in a business or commercial setting that is of direct relevance to your development as an experienced computer scientist. You will apply the knowledge and skills from taught modules to a real problem in computer science at a professional level. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills. Placements will involve a substantial technical role in the host organisation. Individual placements are subject to availability and approval by the module leader.

This module aims to provide students with the experience of working in industry in order for them to apply the knowledge and skills acquired in an academic environment to an industrial setting.

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COM3021: Data Science at Scale

Data science relies on large amounts of data to be effective and many commercial and scientific applications require the analysis of large quantities of heterogenous, noisy data on distributed machines. This module will examine the ways in which algorithms for data science can be implemented for large data and will discuss new algorithms specifically designed for large scale data. You will also work with large-scale distributed and cloud systems for storing and computing with big data.

Through theory and practice this module aims to equip you with an understanding of the principles of distributed computing, particularly on cloud-based systems, the ways in which data can be stored and accessed to allow efficient computation, and efficient algorithms for large-scale computation.

Distributed cloud computing will provide you with the underpinning knowledge required to develop and implement machine learning and artificial intelligence algorithms on distributed high-performance computing systems.

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COM3031: Probabilistic Machine Learning

This module provides an advanced exploration of machine learning and artificial intelligence, focusing on probabilistic modeling, inference techniques, and structured learning methods. It also examines key theoretical foundations alongside advanced techniques, such as Bayesian Neural Networks and Variational Autoencoders, which enable uncertainty quantification and probabilistic generative modeling.. The module delves into Bayesian theory, its role in handling uncertainty, and its connections to approximate inference methods and information theory. Students will also explore techniques for modeling temporally and spatially structured data, including Hidden Markov Models. Additionally, the module introduces reinforcement learning. By integrating probabilistic reasoning, approximate inference, and structured learning, this module equips students with the theoretical depth and practical skills required for tackling complex machine learning problems.

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ECM3401: Individual Literature Review and Project

This is the module in which everything you have learnt in your Computer Science studies comes together in a substantial piece of individual project work, involving initial research and literature review, and specification and design of a software system, followed by implementation, testing, evaluation, and demonstration of the system. You will work under the supervision of an individual staff member who will provide guidance and advice as appropriate.

The aim of the module is to enable you to consolidate the knowledge, understanding, techniques and skills acquired over the previous two years through the specification, design, implementation, testing, evaluation and demonstration of a software system. The module includes both initial research into the project area (including production of a literature review) and production of the system itself following an appropriate development method.

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COM3024: Computer Vision

How do we recognise objects and people? How can we catch a ball or navigate a busy room without collisions? These everyday tasks have challenged AI scientists for decades. Recent advances in computer vision have led to major improvements in applications such as face detection, body tracking, autonomous vehicles, and action recognition.

This module introduces the fundamentals of computer vision, covering both classical and state-of-the-art methods. You will gain a theoretical understanding of key algorithms, along with practical skills in image processing, feature extraction, object detection, segmentation, and deep learning for vision tasks. The course also explores 3D vision and modern topics such as video analysis and low-shot learning, providing a broad foundation for solving real-world vision problems.

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COM3029: Social Networks and Text Analysis

The rise of the Web has created huge datasets relating to the interaction of users and online content. Much of this content is relational and is best understood using a network perspective (for example, hyperlinked web pages; users linking to content; users linking to users on social platforms). Much of this content consists of unstructured text (for example, webpages, blogs, social media posts) that requires computational methods for analysis at scale. In this module you will learn the core principles of social network analysis and computational text analysis, enabling you to gain insight from the rich data available on the Web.

The aim of this module is to equip you with a range of knowledge and skills needed to make effective use of data from the Web. This module will cover various topics in social network analysis and text analysis, which together allow relational and unstructured text data to be analysed at scale. The module will be taught using the Python language and various open source packages.

The module will be taught in lectures and associated practical work, together with individual self-study and labs. Lectures will introduce the topics of social network analysis and text analysis, accompanied by practical exercises based on lecture material. Assessments will include assessed pitch-deck presentation of the mini-project and an individual mini-project involving the applications of social network and text analysis.

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ECM3408: Enterprise Computing

The vast majority of businesses now rely upon well-designed, functional, efficient and secure IT systems to carry out their day-to-day operations and to guide their business strategy.  This module introduces you to the techniques required to enable the development of systems that can operate across multiple sites, perhaps even multiple countries, in a secure and efficient manner.   In addition, the module highlights the issues and opportunities that can arise from the creation and storage of large-scale datasets.  This module will be appropriate for any student interested in the development of enterprise-level software who is studying a programme with significant programming content.

The aim of this module is to introduce you to the enterprise-level techniques used to implement large-scale distributed systems in heterogeneous environments and to consider issues such as interoperability, performance, security and persistence of information within those systems. The module also aims to provide you with an understanding of the latest internet technologies used to assist enterprises in their operation, such as service-oriented architectures, web services and cloud computing.

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ECM3412: Nature-Inspired Computation

There is a wide range of tasks, including product design, decision-making, logistics and scheduling, pattern recognition and problem solving, which traditional computation finds either difficult or impossible to perform. However, nature has proven to be highly adept at solving problems, making it possible to take inspiration from these methods and to create computing techniques based on natural systems. This module will provide you with the knowledge to create and apply techniques based on evolution, the intelligence of swarms of insects and flocks of animals, and the way the human brain is thought to process information. This module is appropriate for any student with an interest in natural systems, optimisation and data analysis who has some programming and mathematical experience.

This module aims to provide you with the necessary expertise to create, experiment with and analyse modern nature-inspired algorithms and techniques as applied to problems in industry and industrially motivated research fields such as operations research.

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ECM3422: Computability and Complexity

It is popularly supposed that there is no limit to the power of computers to perform any task, so long as it is sufficiently well defined, and to do so quickly and efficiently. In fact this is not so, and it can be proved mathematically that there are well-defined computational tasks which cannot, in principle, be performed by computers as we know them; and other tasks which, while they can be performed, cannot be completed in a feasible amount of time. This module will introduce you to the Turing Machine model of computation which underpins the fundamental theories of computability (concerned with what can be computed at all) and complexity (concerned with how efficiently things which can be computed can be computed). These theories will be introduced in a precise and formal way, and the main results and theorems will be stated and proven.

The overall aim of the module is to introduce the mathematical basis and practical implications of the classical theory of computability and complexity and to consider the extent to which this is still relevant to modern developments in computing.

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ECM3428: Algorithms that Changed the World

Algorithms are precisely defined procedures designed to solve computational tasks: they are the life-blood of computing. This module is designed to highlight the importance of algorithms in Computer Science, providing you with an understanding of what algorithms are, how they can be specified and evaluated, and what they can be used for. These general ideas will be illustrated throughout by means of an in-depth study of a range of example algorithms which have played an important part in the development of Computer Science and underpin current computing practice. The prerequisite knowledge may be obtained from two first-year computer science and mathematics modules.

PRE-REQUISITE MODULES: ECM1400, ECM1414, ECM1416

In this module, you will build on the knowledge acquired in ECM1414 (Data Structures and Algorithms) with a more systematic exploration of a range of different types of algorithms and the principles of their design and analysis. A range of specific computational problems will be covered (e.g., operations on strings, graphs, and other data structures, numerical problems), and different algorithms for these problems analysed.

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ECM3446: High-Performance Computing

The demand for ever-increasing computational power drives the development and exploitation of high-performance computing that underpins leading edge research in computationally intensive scientific and engineering fields. This module is designed to equip you with a solid foundation and useful skills in high-performance computing. In this module you will learn about current high-performance computer architectures and how the computer architecture influences the performance of algorithms and programs. You will also develop skills in parallel algorithm design and parallel programming, and will gain experience of using a high-performance computing system.

PRE-REQUISITE MODULES : ECM1416, ECM2433

This module aims to provide you with a thorough grounding in parallel programming and the architectures used in high-performance computing. After presenting the fundamental ideas and basic concepts of high-performance computing, the module outlines the architectures, components and parallel programming of high-performance computers. The module will introduce you to recent developments and future trends in architecture and algorithms in high-performance computing.

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EMP3001: Commercial and Industrial Experience

This module will provide you with an opportunity to undertake practical work experience in a business, commercial or public sector setting that is of direct relevance to your development as an experienced professional. You will apply the knowledge and skills from taught modules to authentic problem solving in the workplace, which will give you important insights into your potential job role once you graduate from university. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills. Placements will involve taking responsibility for a substantial project, which may be a problem to be solved in the host organisation, in line with your degree programme. Placements are subject to availability, approval by the module convener and full compliance with important Health and Safety procedures and requirements. Placements are normally three months some time during May-September, finishing before autumn classes start. Placements must be a minimum of six weeks full time. It is understood that this will entail around 210 hours of supervised work in order to generate the depth of experience equivalent to the 125 hours of self‑directed study on a focused topic specified under the regulations, as workplace activity is not counted directly as academic study International placements are allowed. Placements can be paid or volunteer.

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MTH3019: Mathematics: History and Culture

Over the course of its history, mathematics has been shaped both by the subject’s own internal logic, as well as by the nature and needs of the society it which it was developed and transmitted. This module gives you the opportunity to see how the mathematics studied today has evolved over the centuries, and how mathematics relates to wider issues in culture and society. Through a mixture of lectures, student-led presentations and guided study involving the research and writing of essays, you will become familiar with selected aspects of the development of mathematics and its applications throughout history.

The aim of this module is to give you an appreciation of the historical development of mathematics and of its place within the wider culture. By studying a number of specific topics, you will become familiar with the changing nature of mathematics and its role throughout history. This includes how various cultures have been influenced by numbers, geometry, algebra, calculus and the full range of mathematical ideas. Topics will be drawn from particular areas of mathematics, such as numbers, geometry, algebra, calculus, as well as from the philosophy and foundations of mathematics.

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MTH3024: Stochastic Processes

A stochastic process is one that involves random variables. A large number of practical systems within industry, commerce, finance, biology, nuclear physics and epidemiology can be described as stochastic and analysed using the techniques developed in this module. The systems considered may exist in any one of a finite or possibly countably infinite, number of states. The state of a system may be examined continuously through time or at fixed and regular intervals of time.

The probability models considered in this module have a common thread running through them: that the behaviour of the system under consideration depends only on the state of the system at a particular point in time and a probablistic description of how the state of the system may change from one point in time to the next. The systems considered may exist in any one of a finite (or possibly countably infinite) number of possible states and the state of the system may be examined continuously through time or at fixed (and regular) intervals of time. A large number of practical systems within industry, commerce, finance, biology, nuclear physics and epidemiology, can be described and analysed using the techniques developed in this module.

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MTH3028: Statistical Inference

Statistical inference concerns how we use data to describe and predict the world. It is important in a wide range of sectors, including finance, insurance, economics, medicine, retail, industry, sport, environment and government, to name only a few. This module provides a foundation in modern statistical inference from a frequentist perspective and will benefit anyone considering a career in the data sciences. We introduce key inferential concepts and procedures, study their theoretical properties, and apply them to a range of statistical models using the statistical programming environment 'R'.

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MTH3041: Bayesian Statistics, Philosophy and Practice

Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data analytics, pattern recognition and machine learning as well as numerous inferential procedures used across science, social science and the humanities.

This module will cover the Bayesian approach to modelling, data analysis and statistical inference. The module describes the underpinning philosophies behind the Bayesian approach, looking at subjective probability theory, the notion and handling of prior knowledge, posterior inference, and how this approach differs to classical approaches to statistics. It will explore simulation-based inference in Bayesian analyses and develop important algorithms for Bayesian simulation by Markov Chain Monte Carlo (MCMC) such as the Gibbs sampler and the Metropolis-Hastings algorithm. Finally, we’ll apply the techniques and tools developed through the module to fit a wide range of models using modern Bayesian inference software, enabling you to apply techniques discussed in the course to a variety of real datasets.

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