BSc (Hons) Data Science Bachelor's degree at Keele University

This module introduces you to aspects of professionalism and ethics, communication, teamworking, and writing styles: these are also useful transferable skills in science and engineering more generally. The module provides you with important transitional skills that will be built upon in future modules and subsequent careers. The module provides guidance on enhancing employability skills, and advice on working in teams. Delivery of the module will include workshop activities providing you with op

In the first part the module introduces the fundamental concepts underlying computer programming, together with techniques for applying these using Python. The module has a strong practical element. Examples will come from various areas of Computer Science and Data Science. You will gain skills of producing, compiling, and debugging simple programs in Python. These skills will be refined in later modules with applications in various areas of Computer Science. In the second part this module teach

Mathematics underpins much of modern-day Data Science and is a quantitative language for how to discuss concepts and ideas in a formal setting. This module will introduce students to areas of Mathematics most relevant to these areas, including vectors and matrices, functions, graph theory, logic, information theory, probability and statistics, as well as a toolbox of essential mathematical techniques. Students will learn how mathematical abstractions can be used to represent real world scenarios

You will learn fundamental concepts of computer hardware and software and achieve a foundation for other computer science modules. You will obtain an overview of computer systems, bridging theoretical concepts with practical applications and gain an understanding of the capabilities and limitations of modern IT. You will see logic design for core hardware components, the architecture of operating systems, essential features of programming languages, a selection of computing applications, hardwar

This module provides a comprehensive introduction to web technologies and databases, equipping students with the practical and analytical skills to design, develop, secure, and deploy modern web applications with robust data-driven backends. Upon completion, students will be able to apply industry-standard tools and best practices in contemporary web and database development.

This module provides an introduction to common techniques for exploring, summarising and modelling data. The module develops transferable skills through solving problems, modelling and the use of statistics to understand quantitative information. Emphasis is placed on understanding the meaning behind the data and on the importance of the correct presentation of findings.

Scientific computing is one of the cornerstones of modern applied data analysis. This module gives the opportunity to further develop skills of programming and problem solving as well as application of mathematical results to real-world scenarios, especially those involving the analysis and interpretation of large data sets. As a result students will also possess a valuable, transferable skill, necessary in a large number of data science industries, for example, engineering, meteorology and fina

This module will provide students with an introduction to artificial intelligence. Topics will including neural networks, deep learning, evolutionary algorithms and neuroevolution. The focus will initially be on understanding the underlying principles, structures, algorithms and capabilities of AI systems, and then on writing programs that use appropriate machine learning software libraries to pre-process data and apply AI to tasks such as text analysis, data analytics and visualisation, compute

An opportunity to pursue a personal interest in data science, learning and applying programming and technical skills that complement and extend those taught within the curriculum, under guidance from a staff member.

This module will expand on the range of artificial intelligence themes introduced earlier in the degree. It will allow students to explore in greater depth, selected research-led topics at the forefront of current thinking in the rapidly evolving field of AI. The module will also provide students with insights and understanding of fundamental issues relevant to data ethics, security and AI governance. Students will acquire practical data science and machine learning skills such as dataset analys

This module introduces cloud technologies and distributed computing, focusing on both principles and practical applications. You will explore big data technologies like Hadoop and NoSQL databases and gain hands-on experience with cloud platforms such as Amazon Web Services. You will also develop skills in digital communication principles, as well as cloud and network security and compliance.

This module enables students to integrate and apply theoretical knowledge and problem-solving skills to a relatively large or complex technical, software, programming, research or business problem within the Data Science discipline.

This module gives students interested in pursuing teaching or training as a career the opportunity to gain experience in the world of teaching in schools, colleges and other educational settings. You will develop skills highly valued by employers such as communication, and interpersonal skills, whilst learning to address individual needs and developing engaging teaching materials. You will be supported via a range of activities to prepare for your placement and have the choice to source your own

Cybersecurity sits at the heart of today's interconnected world, where every organisation faces complex and evolving digital threats. This 30-credit module gives you the knowledge, practical skills, and professional insight needed to meet those challenges with confidence. Bringing together a solid foundation in cybersecurity principles with application-focused content, the module blends lectures, hands-on labs, and team-based projects to simulate real-world problem-solving. You will explore the

Independently investigate an advanced topic in Data Science, applying programming and technical skills to design and implement a solution or demonstration that extends knowledge beyond the taught curriculum, under academic supervision.

This module illustrates the application of statistical techniques to health related research. Methods are applied using data from real-life studies, and the importance of statistics for making healthcare decisions is emphasised. No prior knowledge of medicine or biology is required. The module commences with a revision of hypothesis testing and statistical inference procedures. This is followed by four main topics: epidemiology, survival analysis, clinical trials and meta-analysis. Epidemiology

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