New sources of data in a wide range of formats contain valuable information, but extracting this information is often challenging using traditional tools. This module introduces modern techniques for analysing such data and demonstrates how they may be put into action. Methods for handling structured and unstructured data are discussed, including techniques for the analysis of textual data.
The challenge of data mining is to transform raw data into useful information and actionable knowledge. Data mining is the computational process of discovering patterns in data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and data management. This course will introduce key concepts in data mining, information extraction and information indexing; including specific algorithms and techniques for feature extraction, clustering, outlier detection, topic modelling and prediction of complex unstructured data sets. By taking this course you will be given a broad view of the general issues surrounding unstructured and semi-structured data and the application of algorithms to such data. At a practical level you will have the chance to explore an assortment of data mining techniques which you will apply to problems involving real-world data.
Data analysis is changing. New sources of data in a wide range of formats contain valuable information, but extracting this information is often challenging using traditional tools. This module introduces modern techniques for mining such data and demonstrates how they may be put into action. Methods for handling structured and unstructured data are discussed, including techniques for the analysis of textual data.
The module provides an introduction to data analytics and data mining. It will combine practical work using R and SQL with an introduction to some of the theory behind standard data mining techniques.
Companies nowadays have collected a large volume of data from various sources. This module aims to introduce the key concepts of using ‘Big Data’ to improve marketing activities. Specifically, it focuses of the use of data mining techniques to manage customer relationships. Relevant marketing issues such as customer surveys, profiling/segmentation, communications, campaign measurement, satisfaction, loyalty, profitability, social media and other current topics will be discussed with regard to how data mining and analytical approaches can be used to improve marketing decision making. In this module, students will get hands-on experience and will be introduced to software commonly used in marketing departments and organisations. Thus, this module seeks to equip students with key skills needed to manage real marketing decisions based on marketing data.
“The purpose of computing is insight, not numbers” (Hamming, 1962). Data science is all about gaining insight from the large amounts of data we are surrounded by. In our digital world, engineers need to be able to use a range of tools, technologies and platforms to make sense of data and tackle complex engineering problems. In this module you will - Become confident in using a whole range of data science techniques - Enhance your digital skills - Learn about how, where and when to use a range of important computational tools, technologies and platforms This module will help become proficient in the digital skills you need for everyday and engineering tasks throughout your degree and beyond.
This module will help you become proficient in data analysis and computational methods with coding that you will need for solving engineering challenges throughout your degree and beyond.
The aim of this module is to present a range of data science concepts, including dealing with administrative and big data sources, and to present some basic methods for data analysis.
Data visualisation is the process of summarising and communicating the information in a dataset through graphics. This course examines what makes good visualisations, and how this depends on the audience and purpose of the visualisation and the type of data being displayed. The link between good graphics and an understanding of human perceptual and information-processing capacities are discussed. These principles are put into practice by using the R programming language to construct and deploy high quality visualisations.
Welcome to the Data Visualisation module! In this course, you would learn about the terminology, concepts and techniques behind visualising data, and will get to use a range of tools to get experience of creating visual representations of data. You will gain an understanding of how humans perceive data, and why certain techniques can greatly enhance the effectiveness of any visualisation. We will look at example images to critique them, building up knowledge about what works, and what doesn't. The course will include a mix of lectures, tutorials, seminars and hands-on exercises.
Data organise our present and shape our future. Those data are never neutral because they are the product of human labour, of choices made by people about what data to record, how to record it, and who is best equipped to do that recording. Drawing on work from intersectional feminism, anti-colonial theory, and infrastructure studies, this module takes a justice-led approach to data as both products and producers of culture. It examines the ways that the datafication of culture has produced predictive systems that police us, structures that define us, and products that simulate us. It explores the connections between historical forms of data production and present day inequities. It discusses the value, purpose, and variety of justice-led approaches to analysing data and culture. And it considers how we might creatively resist, reimagine, and remake the relationship between data, culture, and social justice. No technical or theoretical knowledge is required to take this module. It is open to all, whether you want to develop a justice-led approach to thinking about the intersections of data and culture, or you want to work with data to apply justice-led thinking to your analysis of culture.
The module will introduce contemporary computational methods for fluid flow analysis, with a specific focus on techniques that use simulation or experimental data. The module will cover aspects of flow stability, model order reduction and pattern identification, as well as data-assimilation techniques and machine learning for system identification. Through a blend of lectures and hands-on laboratory sessions, the module will provide students with the practical knowledge required to implement and apply these methods, together with a solid understanding of fundamental fluid mechanics and mathematical concepts underpinning their use.
The application of scientific techniques is increasingly embedded in archaeological studies and is an area where the UK currently leads the world. Techniques such as dating methods, the use of isotopes to reconstruct past diet or human migrations and the sequencing of ancient DNA are responsible for many major recent breakthroughs in our understanding of the past. But rather than teach students to produce scientific data, or bog them down with scientific equations, this module aims to give the students the skills required to be consumers of archaeological science. They will become familiar with the scientific literature and learn to cast a critical eye over scientific data; interpret it for themselves and engage in the archaeological debates arising from the science.
Decision making and analysis are among the most critical skills of successful project managers throughout their career. Significance of these skills and the outcome of decisions in a project’s success or failure have been emphasised in theory and highly appreciated in practice of project management. This module introduces the characteristics of decisions and decision making in project environments. Different theoretical and practical approaches, styles and methods of making and analysing project decisions will be discussed and practiced throughout the module.
Literary history is often told in epochs. In particular, it can be useful to understand the world in relation to some or other idea of “modernity”: for example, English literary studies is often organised through conceptions of the early modern, the modern, and the post-modern. But many influential constructions of modernity assume and promote Eurocentric ideas of progress, development, and history. This module invites you to interrogate these ideas. The module begins with work that reveals the cultures of violence and inequity that are instituted by imperialist constructions of modernity and civilisation. You will then learn to work with debates that have been conducted through formulations of ‘postcolonial studies’, ‘subaltern studies’, ‘diaspora studies’, ‘world systems’, ‘history wars’, ‘world literature’ and ‘decolonisation’. Across the module, you will explore fictions of various genres from Africa, the Americas, Australia and other parts of the world, and you will consider the importance of literature to debates about race, law, identity, belonging, political and economic geography, and citizenship.
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing. This module focuses on the latest advances in deep learning and will allow you to start to understand, implement, and critically appraise recently published research, as well as give you some of the wider context of research in the field. As part of this module, you will have the opportunity to extend on the latest research, contributing with your own ideas, critical thinking, and experimental design skills. Due to the active and effervescent nature of the field, deep learning almost equates to research. Our objective is to equip you with the knowledge and skills required to engage with research as it emerges -- to be able to distinguish high-quality work that will change the course of the field from simple buzz. In this process, we will teach you how to build your own scientifically sound experiments and how to identify contributions you can make that would be valuable for the deep learning community.
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of massively parallel compute coupled with large datasets. This module explores how deep learning can be applied to real world data by implementing models through combinations of pre-built building blocks.
This module examines the patterns of life in deep-sea environments & the processes that govern those patterns.
