The main goal of this module is to equip you with essential Python programming skills and foundational mathematical concepts crucial for AI and Data Science. Through hands-on learning, you'll develop the ability to solve real-world problems, process complex data sets, and apply key mathematical techniques like probability and matrix operations. Formative assessments will support your learning, leading to a final practical assessment that prepares you for advanced studies. This module sets the stage for your success in AI and Data Science.

The main goal of this module is to explore the essence of AI and Data Science, their origins, and their roles in solving real-world challenges. You'll delve into the duties and skills of data professionals, emphasising effective communication and ethical considerations. The module also covers the legal and societal impacts of AI, while promoting teamwork through hands-on projects that tackle AI and Data Science challenges. Supported by industry talks, you'll learn to formulate problem statements, select appropriate methods, and communicate findings effectively, preparing you for a successful career in this dynamic field.

A large part of the Master's involves completing the industry or research related project. This starts with the students selecting an industry or research partner, undertaking a placement in June - July, and then submitting a written dissertation of up to 20,000 words in early September.

This is primarily a self-study module designed to provide the foundation of the main dissertation, at a level considered to be of publishable quality. The project also offers students the opportunity to apply their technical skills and knowledge on current world class research problems and to develop an expert knowledge on a specific area.

The topic of the project will vary from student to student, depending on the data science specialism (eg computing may involve the design of a system, while specialism in data analytics, health or environment, are likely to be more applied, perhaps focusing upon inherent data structure and processes).

This module provides a practical introduction to statistical learning from model training to deployment. The student will learn about optimisation as a means for model training; the statistical principles and methods that underpin model selection, and the application of classification and regression models to real data problems.

A variety of supervised learning models and their estimation will be covered, including linear models and their connection to kernel based methods; feed-forward neural networks; and tree based models. Tree based models will be extended to their use in random forests and gradient boosted models. In addition topics relevant to big-data problems including dimensionality reduction variable selection; and stochastic gradient descent will be covered.

Students will be introduced to the widely-used statistical computing package R, which will be the primary tool for data analysis and modelling in this module. In addition to learning how to use R effectively and efficiently, students will also be encouraged to compare and contrast with their existing or developing knowledge of general-purpose languages such as Python.

This module aims to equip students with a deep understanding of the rapidly evolving field of Artificial Intelligence (AI), focusing on both cutting-edge methods and applications. You'll 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 will immerse students in advances in ecological research and conservation that provide key skills for working as an ecologist in the era of Big Data. Teaching is delivered by world-leading researchers who are experts in biodiversity from coral reefs to tropical forests and freshwater lakes, ensuring a deep understanding of how data science can generate actionable insights for global conservation. Throughout the course, students will gain an understanding of the principles behind data science tools and techniques, which will help them develop both a fundamental understanding of the natural world and urgent solutions to the global biodiversity crisis. The curriculum is dynamic and will adapt annually to address contemporary issues.

Indicative topics include:

1. Why do we need data science for biodiversity?

2. Big Data: advantages, challenges and solutions

3. Automating species ID for citizen science (AI, machine learning)

4. Tracking animal movements underwater (acoustic telemetry)

5. Quantifying 3D habitat structure (photogrammetry)

6. Biodiversity soundscapes in a noisy world (bioacoustics)

7. The ecological role of colour (machine learning)

8. Scaling up: from animal behaviour at global species distributions (geospatial)

9. Extended reality for ocean empathy (XR)

10. Responsible data science for biodiversity

Workshops offer in-depth exploration of advanced topics, such as AI’s role in predictive ecology, cutting-edge ecological technologies, biodiversity beyond species richness, data visualization strategies, and innovative data-driven solutions to the biodiversity crisis. Our interdisciplinary approach blends ecological and computational perspectives, preparing you for in-demand roles in the evolving ecology sector.

After introducing the topic of forecasting in business organisations, issues concerned with forecasting model building in regression and its extensions are presented, building on material covered earlier in the course(s). Extrapolative forecasting methods, in particular Exponential Smoothing, are then considered, as well as Machine Learning / Artificial Intelligence methods, in particular Neural Networks. All methods are embedded in a case study in forecasting in organisations. The course ends by analysing how forecasting is applied to operations and how forecasting can best be improved in an organisational context.

This module introduces students to the fundamental principles of Geographical Information Systems (GIS) and Remote Sensing (RS) and shows how these complementary technologies may be used to capture/derive, manipulate, integrate, analyse and display different forms of spatially-referenced environmental data. The module is highly vocational with theory-based lectures complemented by hands-on practical sessions using state-of-the-art software (ArcGIS & ERDAS Imagine).

In addition to the subject-specific aims, the module provides students with a range of generic skills to synthesise geographical data, develop suitable approaches to problem-solving, undertake independent learning (including time management) and present the results of the analysis in novel graphical formats.

The main goal of this module is to explore the development and optimisation of intelligent, autonomous agents capable of outperforming human capabilities in various tasks. You'll 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.

The main goal of this module is to equip students with the expertise to design and implement robust technology platforms essential for effective AI and Data Science systems. You’ll 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.

The main goal of this module is to provide students with cutting-edge knowledge in natural language processing (NLP) as applied in both industry and research. You'll learn how to collect, clean, and analyse language data at scale, using methods ranging from rule-based to deep learning techniques. The module covers 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.

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 is now capable of solving many industrial-scale problems to proven optimality.

The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.

This module will equip the student 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, students will learn to translate statistical uncertainty into decision-making processes

Students 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, students will 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.

The purpose of this course is to understand and use mathematical models in making strategic, tactical, and operational logistics decisions. Emerging logistical concepts will be introduced and the associated mathematical modelling needs will be discussed. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics covered are: facility location, network design, warehousing, vehicle routing and scheduling, and Terminal (airport) capacity management.

Depending on students need and level of programming skill, the computer workshops will focus on either solver languages (e.g. GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert, Gurobi Python Interface).