Choose a specialist pathway that aligns with your interests and career goals. Each pathway offers access to the cutting-edge tools and techniques of data science, allowing you to graduate with the skills to build a career in this area.
You will select 3 of the optional modules from your chosen pathway.
Strong computational skills are essential for data scientists. With the application of AI within commercial settings, this has become increasingly important. Enhance your technical skills (through advanced Python programming and the design of Big Data Systems) and develop a wider understanding of AI (exploring topics including Natural Language Processing, Reinforcement Learning, and the design of distributed algorithms). The skills and knowledge gained on this pathway provide an excellent technical grounding for a career in any sector.
Explore the development and optimisation of intelligent, autonomous agents capable of outperforming human capabilities in various tasks. You will learn the core concepts of intelligent agents, from fundamental AI paradigms like rule-based systems, planning and learning, to advanced decision-making algorithms. The module emphasises both classical and modern AI techniques, showing how traditional ideas continue to inspire powerful innovations. Through practical exercises, you'll design, implement, and validate AI algorithms, enhancing your skills in problem-solving, critical thinking and translating complex algorithms into functional code.
Gain expertise in designing and implementing robust technology platforms essential for effective AI and data science systems. You will explore a range of technologies like Hadoop, Spark, and PyTorch Distributed, learning how to select, configure and optimise them for large-scale, high-performance computing. The module focuses on principles of system architecture, distributed machine learning, and scalability, with real-world case studies and industry insights. By the end, you'll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions and implement distributed machine learning models effectively.
Gain a deeper understanding of the rapidly evolving field of Artificial Intelligence (AI), focusing on both cutting-edge methods and applications. You will explore AI's role in areas like cybersecurity, ethical considerations, human-AI interaction and emerging technologies like Quantum AI. The module prepares you to apply AI to real-world challenges or pursue research in innovative AI techniques, ensuring alignment with current industry and academic trends. Additionally, you'll develop skills in implementing AI solutions, making ethical decisions and effectively communicating your findings in professional settings.
This module provides you with cutting-edge knowledge in natural language processing (NLP) as applied in both industry and research. You will learn how to collect, clean and analyse language data at scale, using methods ranging from rule-based to deep learning techniques. Learn about key applications like machine translation, sentiment analysis and summarisation, alongside discussions on language models, ethics and bias in NLP. By the end, you'll be able to create scalable solutions for language data challenges, understand current NLP research trends and enhance your skills in independent study, critical thinking and effective communication.
Study how data science tools enable organisations to derive insight from their data and to make better decisions. You will develop advanced technical skills in data handling and machine learning methodologies (such as Time Series Forecasting, Supervised Learning, and Optimisation), and the capacity to communicate your findings in a business setting. Develop a range of technical and professional skills that are highly sought-after in commercial data scientists.
This module introduces the topic of forecasting in business organisations. You will engage with issues concerned with forecasting model building in regression and its extensions, building on material covered earlier in your degree programme. You will then consider extrapolative forecasting methods, particularly Exponential Smoothing, and Machine Learning or Artificial Intelligence methods, particularly Neural Networks.
You will engage with a case study about forecasting in organisations, which embeds all the methods covered in this module. The module ends by analysing how forecasting is applied to operations and how forecasting can be improved in an organisational context.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software can now solve many industrial-scale problems to proven optimality.
This module enables you to apply optimisation techniques to business problems. It introduces you to different problem formulations and algorithmic methods that guide decision-making in businesses and other organisations.
This module introduces the fundamental methods and approaches from the interrelated areas of data mining, statistical/machine learning, and intelligent data analysis. It covers the entire data analysis process, including:
The module focuses on classification, and you will use the R programming language.
Many systems in business and industry operate under uncertainty, with apparent randomness. Ignoring uncertainty when making decisions about how these systems operate can lead to poor system performance. This module introduces important methods for modelling and optimising systems under uncertainty.
Simulation is a powerful technique for gaining insight into complex stochastic models in which analytical tools become intractable. Reinforcement learning is a state-of-the-art method which uses reward signals and feedback loops to search for near-optimal decision-making policies in dynamic decision-making problems.
The possible applications of these methods include:
You will develop the analytical and practical skills needed to analyse and improve system performance in a wide variety of contexts.
The Biodiversity crisis is one of the most urgent challenges facing ecologists today. Study how modern data capture tools and the application of AI have revolutionised research in environmental and ecological applications. You will develop the skills and understanding necessary to use advanced statistical, machine learning and AI methods to address biodiversity challenges in a robust and scientifically valid way.
We introduce you to the fundamental principles of Geographical Information Systems (GIS) and remote sensing and demonstrate how these complementary technologies may be used to capture/derive, manipulate, integrate, analyse and display different forms of spatially-referenced environmental data. We blend theory-led lectures with hands-on practical sessions using state-of-the-art software. Alongside core subject knowledge, you'll build transferable skills in synthesising geographical data, developing problem-solving strategies, managing your time effectively and presenting analysis through innovative graphical formats.
Discover research advances in the field of ecology and conservation that provides key skills for working in the era of big data. You will be taught by world-leading researchers who are experts in biodiversity from coral reefs to tropical forests and freshwater lakes, ensuring deep understanding of how data science can generate actionable insights for global conservation. Throughout the module, you will understand the principles behind data science tools and techniques at the forefront of developing both fundamental understanding of the natural world and urgent solutions to the global biodiversity crisis.
This module will equip you with the understanding and skills to use statistical methods to solve current ecological challenges in a robust manner. By gaining familiarity with both frequentist and Bayesian inference, you will learn to translate statistical uncertainty into decision-making processes
You will have the opportunity to experiment with different ecological data types. By examining case study data sets through the lens of visualisation and descriptive analysis, learn why specific statistical models are required for the different ecological data types.
Examples of challenges that will be investigated include species abundance, and the effects of heterogeneity on this; the use of demographic parameters to model population dynamics; application of statistical models for spatial data; and modelling emerging data types such as citizen science data, environmental DNA and multi-species data.
The knowledge and skills gained in this module are highly sought-after by conservation charities and non-governmental organisations.
