This module requires the learner to consider their particular environment in the context of wider curriculum design and development. It will require the student examines to compare and contrast different curriculum models. Students will explore appropriate models of evaluating education and training at work. This module is taught at M level.
This module will clarify the links between: identifying characteristics of consumers that can be measured or understood; the methods to measure or understand those characteristics; and how such measurements and understanding support marketing decision-making. It will focus upon how ubiquitous data from Internet can be used to understand and gain insight into consumption patterns and customer behaviour.
Understanding what motivates, influences and drives buyers to buy; be they consumers or business purchasers, is a critical area of learning in marketing. This module explores a range of buyer behaviour concepts, models and frameworks, enabling you to critically consider how these apply to different organisations and contexts. Further, this module supports you to connect with learning across your programme, in areas such as customer insight and marketing data analysis, to consider how understanding buyer behaviour can contribute to the development of effective and relevant marketing actions.
This module is designed to introduce you to the human dimension of cybercrime and cyber security. It is not coming from a technical perspective, but instead a critical criminological approach is applied to the topic. This means that we will be questioning theory, policy and practice, and discuss the way that this area might develop in the future.
This module covers two aspects of a pivotal intersection: applying security to defend machine learning and leveraging machine learning to enhance security. The aims at a high level are to: - Investigate security issues around machine learning systems - Review a variety of defence mechanisms for machine learning systems - Explore the use of machine learning in cyber security
Film Noir is one of Hollywood’s perennial cult genres, yet it is notoriously difficult to define, as it essentially amounts to a retrospective invention by critics. This module will attempt an understanding of the term through reference to its cultural contexts, placing the main corpus of the genre’s classics within its original historical moment of the 1940s and 1950s, as well as exploring its later Neo-Noir and global incarnations
This module provides an overview of key approaches to the analysis of quantitative and qualitative data in education. Building on the knowledge you gained about educational research design and data collection, you will learn how to turn your data into research findings, how to ensure the quality of your analysis, and how to present and communicate your findings.
Working with data of various forms is a crucial skill for all engineers and scientists. This module introduces students to working with, analysing and processing various different forms of data. The module focusses on ensuring students have a thorough grasp of the appropriate use of statistical and graphical measures to make decisions on data, and the basic practical tools and techniques required to filter, refine and query data. At its heart, this module provides the grounding for students to be able to perform Exploratory Data Analysis (EDA).
This module is intended to provide you with a blend of theory and current practice in organisational decision making and data management. The module critically discusses the complexity of organisational decision making by identifying key concepts and relevant theories. The module examines the role of knowledge in the contemporary organisations and explore the ways it can be used for better data management. Existing models for analysing decision making processes are examined, as well as how information systems and analytical tools can be used to tap into (big) data and support and enhance decision making within different organisational contexts.
This module will provide you with knowledge and understanding of data analytics to support decision making in modern marketing. You will explore the role of data analytics in marketing strategy and develop an insight into how to apply analytical tools to solve marketing problems, achieve ethical and sustainability goals and foster innovation. You will have the opportunity to learn through real-world case studies and experiment with different types of analytics. You will learn the principles, methods, and uses of data analytics in achieving marketing and business goals.
Given the importance of data analytics, this module provides students with a systematic and comprehensive understanding of the fundamentals of applied statistical modelling. It shows how statistical analysis can be used to solve civil and environmental engineering problems, using real-world case studies whenever possible. Exploratory data analysis, hypothesis testing, and regression analysis are main topics covered in this module. The main focus will be on developing regression models. Students will gain hands-on experience in using statistical software.
GGES 6018, Data Collection & Research Methods for Sustainability and Environmental Science, is a module which aims to equip students on the MSc Sustainability and MSc Environmental Science programmes with the skills necessary to plan and undertake independent research as part of their studies and later in their chosen careers. Students are introduced to different research methods (quantitative, qualitative and mixed methods), with an initial focus on core quantitative research methods. They are then given the option to either continue learning quantitative research methods or to switch to receiving complementary training in qualitative methods. In the first part of the module, students receive instruction on the fundamentals of quantitative data analysis. They are provided with relevant examples in Sustainability and Environmental Science and are given an opportunity to practice with these and write a quantitative report which contributes to the assessment of the module. They are also introduced to R programming language, which will be used throughout the module for all quantitative analyses. The second part of the module focusing on further quantitative methods aims to introduce the students to statistical techniques relevant to data science applications. The alternative option focusing on qualitative methods aims to provide training on key concepts used in qualitative research. Students are also given an opportunity to apply the skills acquired in this part of the module to a project leading to a research report, which will also form part of the assessment of the module.
GGES3006 Data Collection & Research Methods for Sustainability and Environmental Science, is a module which aims to equip students with the skills necessary to plan and undertake independent research as part of their studies and later in their chosen careers. Students are introduced to different research methods (quantitative, qualitative and mixed methods), with an initial focus on core quantitative research methods. They are then given the option to either continue learning quantitative research methods or to switch to receiving complementary training in qualitative methods. In the first part of the module, students receive instruction on the fundamentals of quantitative data analysis. They are provided with relevant examples in Sustainability and Environmental Science and are given an opportunity to practice with these and write a quantitative report which contributes to the assessment of the module. They are also introduced to R programming language, which will be used throughout the module for all quantitative analyses. The second part of the module focusing on further quantitative methods aims to introduce the students to statistical techniques relevant to broader applications. The alternative option focusing on qualitative methods aims to provide training on key concepts used in qualitative research. Students are also given an opportunity to apply the skills acquired in this part of the module to a project leading to a research report, which will also form part of the assessment of the module.
This module will cover the purposes and use of different methods for data collection in education research. It will address the design and use of questionnaires, different types of interviews and classroom observations. At the end of the module, students will have developed their skill in designing data collection instruments in connection to each of the three methods under focus and their critical understanding of the affordances and limitations of different methods.
This module studies how data is generated, valued, and monetised within digital ecosystems, as well as the ethical, legal, and technical challenges surrounding data ownership, privacy, and regulation. For example, how can we manage a music dataset produced by artists and used to train a generative AI model? What are the technical solutions to support selling and profit distribution of the generated model? What are the ethical and legal implications for artists and other actors involved? The module covers the data value chain, from collection and storage to integration, analysis, distribution, and monetisation, and the data governance issues associated with it.
Data is material. It is produced by people, it is made possible by resource extraction, it needs power to survive, it inhabits and resculpts the landscape. The use of data, then, contributes to climate catastrophe, but that role can be hard to see, hidden as it often is by a veneer of utopian hype that surrounds the information technology sector. Drawing on scholarship from digital media studies, environmental history, computer science, science and technology studies, climate science, and archival science, this module examines the past, present, and future intersections of data and the natural environment. It lifts the lid on the countercultural origins of techno-utopianism. It examines the environmental degradation and injustices that techno-utopianism has and continues to hide (e.g. the instrumentalisation of personal climate responsibility). And it opens a pathway for building an intersectional and justice-oriented data environmentalism.
This module aims to: • Introduce students to the UNIX operating system, to the UNIX command line, and to standard UNIX tools (e.g., vi editor, ed, sed and awk) • Introduce students to version management systems • Provide a grounding in the use of database management systems and SQL • Introduce students to Unix tools for document preparation, software development and system administration
In this module you will develop strategies and skills to integrate data management into humanities data science practices and methods. Over the course of the semester you will learn about good practice guidelines used in humanities research data management and develop skills to interpret and communicate them to a diverse audience of practitioners and researchers. Practical exercises developing data management strategies will enhance your understanding of debates about humanities data science and data driven research in the humanities. By the end of the semester, you will be prepared to situate data science methods in (inter)disciplinary humanities thinking and practically apply them to professional contexts.
Having learned in semester one how to develop and optimise code to generate new and interesting data, you will now learn how to handle the resulting data and maximise the information retrieved. This module provides training in advanced numerical methods that will allow in-depth understanding and solving of problems in physical chemistry, computational chemistry, and spectroscopy. It will also provide transferable skills that can be applied to other areas such as data science and quantitative finance. It involves learning to solve problems on a computer by developing code in Python. The module will also cover data management and procurement, data standards and how to deal with missing or bad data, data reduction, visualisation and error analysis.
