Data Science MSci/BSc (Hons) with foundation year

This module introduces you to fundamental algebraic concepts (sets, functions, basic group theory, linear systems, introductory eigenvalue/eigenvector theory and vector spaces). It should help you develop your computational skills in solving and interpreting linear equations. It aims to provide you with the essential building blocks in algebraic thinking, forming a foundation for more advanced topics in subsequent years.

Compulsory

This module introduces you to key statistics and data science techniques. In this module you will consider data summaries, exploratory data analysis, data visualisation, probability concepts, estimation and hypothesis testing. Explore the predictive analytics methods of regression and correlation and gain hands-on experience using modern software tools to analyse real datasets.

This module includes content on probability and statistics, data wrangling and data visualisation and the practical applications of software tools such as R, Python, MATLAB and Excel.

Compulsory

This module introduces you to single-variable calculus, covering limits, continuity, derivatives and integrals which should create a solid base for later study. Learn about ordinary differential equations, sequences and series. This module includes topics such as foundations of calculus and sequences, differential calculus, integral calculus, complex numbers, an introduction to ordinary differential equations and power series. It also focuses on real-world uses such as optimisation and rates of change, while building your problem-solving skills to prepare you for year two.

Compulsory

This module introduces you to programming and artificial intelligence. Learn the fundamentals of programming, how to write clear and effective code, and develop your problem-solving and algorithmic thinking skills, with a focus on AI applications.

The module focuses on simple AI programming challenges, including games, chatbots and maze solvers, along with introductory material on machine learning, neural networks and generative artificial intelligence. Content includes necessary mathematics, as well as basic data manipulation. Through a combination of lectures, hands-on labs, and collaborative projects, you should gain practical experience in applying AI techniques to solve real-world problems.

Compulsory

Extend your understanding of single-variable calculus into multivariable domains, exploring partial differentiation and multiple integrals. You will be introduced to vector calculus concepts (for example, gradient, divergence, curl) and apply these to geometric/physical problems. Develop knowledge of complex analysis, focusing on analytic functions, contour integrals and basic residue theory. These advanced calculus methods will be linked to real-world or theoretical scenarios to reinforce your computational and problem-solving skills.

Compulsory

Learn the fundamental concepts, models and applications of multiple regression, analysis of variance (ANOVA) and machine learning. These techniques will be explored and applied to a variety of real datasets. Hypothesis testing and estimation will be extended within a statistical data modelling framework.

The methods taught in this module are extensively used in industry, commerce, government, research and development. Module content will address wider social, ethical and professional issues of applying, evaluating and interpreting statistical and machine learning models. Develop key practical skills with appropriate software tools.

Compulsory

Examine broader solution techniques (transform methods, series methods) for both linear and selected nonlinear equations. You will be introduced to Fourier-type expansions and Sturm–Liouville theory for solving boundary-value problems in ordinary differential equations.
Study systems of ordinary differential equations (ODEs), both linear and nonlinear, with a focus on stability and qualitative behaviour. This includes working with simple classifications and elementary solution strategies for partial differential equations (PDEs: wave, heat, Laplace). To reinforce modelling skills, real-world applications and computational approaches will be discussed.

Compulsory

Explore the practical applications of artificial intelligence (AI) across various industries. Get hands-on experience with AI tools and techniques, understand the ethical implications of AI and develop skills to implement AI solutions in real-world scenarios.

Covering key AI concepts and a broad overview of its applications, this module aims to teach machine learning, exploring supervised and unsupervised learning, neural networks and deep learning, with practical exercises to reinforce these concepts. It also examines natural language processing (NLP), where you learn about text analysis, sentiment analysis and language models (including large language models like Copilot and ChatGPT).

Computer vision is also covered, involving image processing, recognition and convolutional neural networks (CNNs), with practical tasks to apply these techniques. Examine robotics and autonomous systems, considering the fundamentals of robotics and the role of AI in autonomous vehicles.

Ethical considerations in AI, including bias, fairness and regulatory frameworks, are examined to understand the societal impacts of AI technologies. This module concludes with case studies of AI applications in various industries such as healthcare, finance and manufacturing.

Compulsory

Get an understanding of optimisation techniques and how they are used in artificial intelligence (AI) and machine learning (ML). Explore the maths behind solving different types of optimisation problems, both with and without constraints and with discrete or continuous variables. This module will explore a variety of optimisation techniques in AI and ML, including gradient descent, batch gradient descent, stochastic gradient descent, Mini-batch gradient descent and Adam. You will investigate advanced topics in optimisation, including second-order, convex, non-convex and constrained, and unconstrained optimisation techniques. You will also get hands-on experience using optimisation methods to train machine learning models and neural networks. These problems outlined in this module are found in areas such as operations research, finance and the sciences, so the skills you develop could be useful in a variety of careers.

Compulsory

This module introduces you to advanced topics in statistics and data science. Using relevant software, learn how to critically examine data and choose and apply appropriate analyses using a range of methods. The material will be set within a range of different contexts taken from industry, medicine, agriculture and engineering. This module will be particularly useful if you are interested in postgraduate study in statistics, or a career in statistics or data science.

Compulsory

The individual project module provides you with an opportunity to apply and integrate knowledge and skills acquired throughout your degree programme in a substantial individual piece of work. The module aims to develop your ability to solve real-world problems, engage in independent research, enhance your professional skills and demonstrate technical proficiency in your chosen subject area. This module should help you develop your project planning and time management skills. It will also test your ability to collect, analyse and validate data as well as your research methodology and literature review technique.

Compulsory

Neural Networks and Deep Learning – 30 credits
Deep learning is a part of machine learning that uses layered neural networks to understand complex patterns in data. Learn the fundamentals of neural networks and deep learning and build practical skills in designing, testing and evaluating neural network models. Explore more advanced deep learning topics and techniques, such as convolutional neural networks, recurrent neural networks and generative models. This module also looks at how deep learning is used in real-world applications including computer vision, language processing and reinforcement learning.

Cryptography and Information Theory – 30 credits
This module integrates higher-level algebraic and number-theoretic concepts (modular arithmetic, finite fields) into the study of cryptography and basic information theory. Learn how abstract algebra underpins classical and modern cryptographic protocols, as well as the fundamentals of encoding/decoding information.

Availability of optional modules may vary depending on student demand.

Optional

Review the fundamental concepts of probability and statistics and develops deeper understanding of statistical models, estimation, inference and stochastic processes. Survey a range of statistical and machine learning models (including supervised learning and unsupervised learning) with applications to real-world datasets. Consider techniques for model evaluation and discuss wider modelling issues such as bias, fairness, under/overfitting, interpretability and scalability.

Build a solid foundation in both statistical methods and modern machine learning techniques, equipping you with a versatile skill set. Apply statistical and machine learning models to practical, real-world datasets. Use industry-standard tools4 and techniques for model evaluation, validation and optimisation, ensuring you are prepared for real challenges in data science.

Compulsory

Develop fundamental concepts, algorithms and architectures of artificial neural networks, deep learning and image processing. These fundamentals are applied and integrated in a broad range of real-world applications with a particular focus on practical and computational challenges in computer vision. This module will provide an in-depth exploration of the latest advancements in deep learning and computer vision. It aims to offer practical experience with real-world applications, enabling you to work on projects that involve building and deploying deep learning models for computer vision tasks.

Compulsory

Build knowledge and understanding of the concepts, algorithms, limitations and challenges of such artificial intelligence technologies, alongside developing practical skills implementing and experimenting with these systems. This module covers advancements in generative AI and reinforcement learning, equipping students with knowledge of technologies that are revolutionising various industries. It emphasises hands-on projects where you can implement generative models and reinforcement learning algorithms, ensuring you gain real-world experience and technical proficiency. It also provides insights into diverse applications of AI across fields such as natural language processing and autonomous systems, preparing students for versatile career opportunities.

Compulsory

This module aims to provide you with sound knowledge of the theoretical and practical underpinnings of the management of big data in centralised and distributed environments. There is a strong emphasis on data engineering, data protection (including GDPR), data governance and data ethics. The module also develops practical skills in data management (such as SQL and NoSQL databases), cloud and distributed computing, and leadership in a data science context.

This module is designed to include the criteria set out by the CMI for the CMI Level 7 Certificate in Strategic Management and Leadership Practice¹.

Compulsory