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.
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.
