Compulsory modules

Introductory Mathematics for Data Scientists
The aim of this module is to help your transition from the methods-based approach in mathematics to the higher levels of understanding and rigor expected at degree level. You will be introduced to fundamental aspects of discrete mathematics and algebra of logic, and more generally, mathematical statements, the meaning of proof, and some proof strategies.

Introduction to Programming
This module will introduce you to the fundamental concepts underlying computer programming and computational thinking. You will gain an understanding of problem-solving techniques and learn to implement your ideas using Python. No prior coding experience is assumed. The aim is to get you up to a good standard of coding by the end of the module. There is a strong practical element and several real-world examples. You will attain a solid practical foundation for modules building upon these fundamental concepts.

Limits, Series and Calculus
This module aims at deepening your understanding and appreciation of the origins of the essential techniques of elementary calculus, explaining why these methods work. Starting with developing important preliminary ideas such as the real numbers, functions, sequences and series, on a more formal basis this leads to a fundamental concept of a limit, serving as a basis for further advances in differential and integral calculus.

Professional Practices in Data Science
The aim of this module is to introduce you to professional practices in Data Science, with input from employers. The key aspects will involve handling data, developing communication, presentation and teamworking skills, ensuring awareness of ethical considerations, as well as computational and programming practices. You will also acquire an additional skill of basic coding in Java in relation to data science problems, complementing programming skills in Python and R provided in other modules.

Linear Algebra
This module begins with an introduction to number theory by looking at topics such as divisibility, greatest common divisors and modular arithmetic with a strong emphasis on rigour. Then you will discuss core concepts from linear algebra including matrix algebra, solving linear equations, determinants, eigenvalues and eigenvectors. You will also cover an introduction to vector algebra with a particular emphasis of on three-dimensional vector algebra and geometry with a discussion on lines and planes, linear independence and bases.

Differential Equations and Multivariable Calculus
Many real-world problems are governed by differential equations, the solution of which can help us understand their properties. For instance, oscillation of a pendulum, and the population evolution of a fish species, can all be described by ordinary differential equations. This module, being a prerequisite for several modules in the second and third years, will introduce you to the basic techniques for solving differential equations and familiarize you with multivariable calculus, including partial derivatives, double integrals, and their applications.

Statistics with Applications in R
This module aims to provide you with a sound knowledge of core statistical concepts, skills and techniques. It will enable you with applying statistical ideas to solve problems from a wide range of disciplines; developing competency in interpreting and explaining solutions of problems in non-technical language; giving fluency in procedural skills, common problem-solving skills and strategies; using statistical tools to analyse real data sets in the statistical software environment R. 

Compulsory modules

Data Science Techniques
Data Science is becoming an increasingly important topic, bordering Computer Science and Mathematics, and with applications in many areas of everyday life.  You will learn to apply processes and techniques to extract essential information from raw data, allowing it to be analysed to discover meaning from the data and allowing it to inform decision-making. You will be introduced to concepts of ethics and security related to the collection, storage and dissemination of data.

Probability
Probability is the mathematics of uncertainty and randomness. You will begin with classical notions of probability associated with the analysis of games of chance using cards, dice, etc., then moving to treatment of probability of random events and further to definitions of statistical independence and conditional probability. Then, you will study discrete and continuous, univariate and bivariate, random variables, covering key concepts of expectation, variance, covariance. Applications to a wide range of theoretical and real-world problems will also be discussed.  

Computational and Artificial Intelligence I
This module will provide you with an introduction to the core computational intelligence topics of evolutionary algorithms and neural networks, and some of the similarities and differences between natural and synthetic intelligent systems. You will gain an appreciation of how AI is used in many real-world scenarios, both within the UK and internationally.

Database Systems
Databases and database management systems are an integral part of any computing application that needs to store data and manage its transactions efficiently. This module introduces you to databases and database management systems by providing theoretical knowledge and practical experience in data modelling, database design, implementation and administration. The module explores essential topics in data management including database security and how databases are integrated with other technologies, especially the Web, for enhanced user interfaces, ease of data access and distribution. 

Visualisation for Data Science
This module will provide you with skills essential for data visualisation, with a general aim of exploration of data analytics within organisations and how this would relate to the information needs of different stakeholders. Evaluation and application of the most appropriate visualisation for given situations will be studied, with benefits and limitations discussed.

Applied Deep Learning
This module will provide you with an introduction to deep learning techniques, with a focus on its capabilities and limitations, as well as programming using a software library to apply deep learning to various tasks including pattern recognition and classification when applied to text processing, computer vision and image processing.

Data Science Projects for Employability
This module gives the opportunity to further develop skills of problem solving in application to real-world scenarios, especially those involving the analysis and interpretation of large data sets. This will contribute to further development of your professional skills,  needed when undertaking employment or research, such as comprehending the problem from business prospective, working on it in depth over an extended period, carrying out analysis, writing reports, presenting and communicating results to different audiences, and working in collaboration with others.

Optional modules

Software Engineering
This module will provide you with up-to-date knowledge and practice of industry standard techniques and processes to undertake team-based development of software and digital products. The module gives hands-on experience of agile software engineering in a self-organising team and the opportunity to compare theory and practice. The module is delivered via a combination of weekly workshops, directed reading and viewing and guest talks from industry professionals. Previous speakers have been from companies such as the BBC, Microsoft, Capgemini and Accenture.

Computer Graphics and Animation
In this module you will learn the fundamental concepts and techniques that underpin computer graphics and animation. You will learn how to create 2D and 3D graphics, and how to time and control animations and visual effects. The module will be delivered by weekly lectures and practical sessions where you will put theory into practice and develop the skills related to creating and composing graphics and animations.

Abstract Algebra
Abstraction is a powerful idea in Mathematics: by focusing on the underlying properties of Mathematical systems we can identify connections and similarities that are not visible on the surface. This module explores these techniques and styles of thinking. The central concept is that of a group; the module establishes some of the fundamental abstract results concerning groups and studies applications to problems in combinatorics, geometry, and number theory. Other algebraic structures such as rings and fields are also introduced, providing a solid foundation for numerous modules in Pure Mathematics.

Professional Experience: Natural Sciences Placement Module (Level 5)
In a competitive job market, practical skills and understanding workplace dynamics are crucial.  This module allows you to gain hands-on experience, enhancing your employability, and the opportunity to gain valuable professional insights via a 75-hour placement. You will be supported by various activities to secure a role and can source your own placement or apply to university-sourced roles.  This module offers flexibility on when and how you complete your placement, enabling you to balance your placement around existing demands.

Compulsory modules

Machine Learning Applications
In this module you will receive in-depth training in the use of machine learning tools and techniques that can be used to analyse real world data and to deliver valuable insight that can be used to provide business services. You will learn about ethics, integrity and working in a global sector. 

Data Ethics and Security
Writing efficient and correct code and learning practical skills are just some of the required skills of a Computer Science or Data Science professional. Alongside these skills, an appreciation of relevant ethics, regulations, governance frameworks and standards must be understood, both in a theoretical and practice sense. This module will help you to appreciate, debate, and apply these ideas, within the UK and globally. You will be assessed on a portfolio of case studies with practical and theoretical components.

Data Science Project
In this module, you will undertake an individual project across two semesters. The module will enable you to integrate and apply theoretical knowledge and problem-solving skills to a relatively complex technical, software, research or business data science problem. You will be allocated an academic supervisor to provide guidance alongside taught lectures. You will develop transferrable skills that will help in your future career including independent and self-directed learning, critical thinking, reflective practice, communication, adaptability, and time management. 

Optional modules

Cyber Security
You will learn the necessary cyber security competencies to protect vital information systems, and their data and services, from unauthorised access, harm or misuse. You will also learn how to keep up-to-date with new recommendations and practice in this volatile and constantly evolving area. You will also gain an appreciation of other important areas such as legal factors, management of systems, risk analysis, and social and human factors. 

Number Theory and Cryptography
Number theory studies properties of the integers, with particular emphasis on the prime numbers. Being one of the oldest areas of Mathematics, it is also an active area of modern research. Recently, classical ideas from number theory have been used in the design of secure ciphers. In this module you will trace the subject development from its ancient beginnings to modern applications, including primality tests, factoring algorithms, modular arithmetic, the RSA and ElGamal ciphers, and techniques for attacking these ciphers.

Data Analysis and Modelling
Modern science is very data rich. Using hands-on exercises, you will gain proficiency in using some commonly used statistical and modelling techniques for analysing scientific data. As well being presented with the theory underpinning the techniques, you will develop the skills required to apply these statistical techniques to real data sets. You will gain some expertise required to judge which technique is applicable in a given situation and whether the results are meaningful.

Advanced Databases and Applications
The core aim of this module is to provide an advanced understanding of database techniques and current issues associated with database deployment. It enables you to develop a detailed and coherent knowledge of distributed database architectures, including techniques for semantic interoperability (schema homogenisation, data integration, query optimisation, and distributed transactions control) between heterogeneous data models and legacy information systems. Data warehousing concepts, architecture, analytical processing techniques and data mining are also covered as one of the distributed database applications. 

Computational and Artificial Intelligence II
This module will expand on the range of computational intelligence (CI) themes, and complementary Artificial Intelligence topics, introduced earlier in the degree. Importantly, the module will allow you to explore in greater depth, selected research-led topics at the forefront of current thinking in the rapidly evolving CI field. After completing this module, you can potentially pursue further research in industry or in education (e.g. as an MSc or PhD student).

Communications and Networks
This module extends your knowledge of principles and practice in communications, computer network, and security technologies and their deployment. Learning about fundamental concepts is complemented by practical activities such as analysing network packets, developing network applications, designing computer networks and cryptography. The module provides valuable transferable skills in critical thinking and problem solving. The module content is applicable to those seeking careers in computing research, network security, network management and other digital communication and network areas. 

Medical Statistics
This module provides you with knowledge and fundamental skills to gain deeper understanding of statistical and epidemiological methods, and applications in public health, health policy, clinical medicine, and health economics. After completing this module, you will be able to choose and perform appropriate statistical techniques, interpret the results of statistical analysis and communicate them in a clear, concise and appropriate manner. This module opens employment opportunities to develop career as medical statistician in the academia, research organisations, and pharmaceutical industry.

Professional Experience in Education
This module allows undergraduates to gain academic credit for their work in schools and colleges. The undergraduate will develop a range of skills and be presented with an opportunity to experience an early taste of teaching for those interested in pursuing teaching or training as a career. The undergraduate will work in a school or college for a total of 64 hours, to be agreed between the placement host and student.  The timing of the placement is flexible to support students’ home, study and work life balance, and to meet the needs/requirements of the placement host, but ideally within a concentrated block rather than dispersed over the whole year. Suggested options include 8 × 8-hour (whole day) sessions, or 16 × 4-hour (half day) sessions. Students will have the flexibility to source their own placement, a useful skill for seeking out future employment opportunities, but also can apply for a range of roles sourced by the university.

Professional Experience: Natural Sciences Placement Module (Level 6)
In a competitive job market, practical skills and understanding workplace dynamics are crucial.  This module allows you to gain hands-on experience, enhancing your employability, and the opportunity to gain valuable professional insights via a 75-hour placement. You will be supported by various activities to secure a role and can source your own placement or apply to university-sourced roles.  This module offers flexibility on when and how you complete your placement, enabling you to balance your placement around existing demands.