. "Environmental sciences"@en . . "Satellite Engineering"@en . . "English"@en . . "Fundamentals of remote sensing"@en . . "20" . "This module will provide a comprehensive understanding of the physical principles of remote sensing. You’ll gain an understanding of the nature of electromagnetic radiation, its key properties and the processes that influence its propagation through space, atmospheres and its physical interaction with matter. It will also provide an introduction to different remote sensing platforms, sensors and data types and practical experience in basic techniques for processing, analysing and visualising remotely sensed data. On successful completion of the module, you should be able to:\n \n demonstrate critical knowledge and understanding of electromagnetic radiation including its characteristics in free space and interactions with matter;\n demonstrate critical knowledge and understanding of the physical principles underlying measurements of electromagnetic radiation using passive and active sensors;\n demonstrate critical knowledge and understanding of the design, technical specifications and deployment modes of different photographic, electro-optical and microwave sensors and how this influences their suitability for different applications;\n demonstrate and apply critical knowledge and understanding of methodologies for sourcing, processing, analyzing and visualising remotely sensed data;\n demonstrate advanced ICT skills for the processing, analysis and visualisation of data and the ability to critically evaluate datasets in numerical and graphical form;\n communicate effectively to a range of audiences with different levels of knowledge and expertise." . . "Presential"@en . "TRUE" . . "Representing and manipulating data"@en . . "20" . "Knowing how to write scripts is essential to everyone who would work with data. This module introduces you to the use of the programming language Python for manipulating data." . . "Presential"@en . "TRUE" . . "Applications in earth observation"@en . . "20" . "This module is designed to give you: \n \n a knowledge of modern EO satellites, software and methodologies; \n the capability to assess when a problem can be tackled with EO and when this is not possible; \n the ability to design a new EO service/solution that could be used to tackle a problem. On successful completion of the module, you should be able to:\n \n understand and explain to others the working principles of Earth Observation satellites and methodologies;\n employ state of the art software to process EO data to make measurements and solve real problems;\n evaluate when a problem can be tackled using EO data;\n design an EO procedure exploiting Copernicus data to tackle specific problems." . . "Presential"@en . "TRUE" . . "Geomatics"@en . . "20" . "Geomatics is the name given to the combination of geographical information systems (GIS), remote sensing and Earth observation (EO), and spatial data acquisition, analysis and visualisation. \n \n These methods and technologies have become vitally important in environmental management, and are being increasingly used by scientists, governmental agencies, environmental NGOs and businesses. \n \n Geomatics enable us to view, question, understand, interpret, and visualize spatial datasets, and by explicitly considering space and location we can unravel underlying relationships, patterns, and trends that may not be apparent when using non-spatial analytical methods. Geomatics skills are highly sought in the job market, as they comprise a powerful suite of problem-solving and decision-making tools. \n \n You’ll be introduced to the basic concepts and principles of Geomatics, with emphasis on using Geographic Information Systems (GIS) and EO (remote sensing). You‘ll also be introduced to essential methods for acquiring and analysing spatial data from varied sources, and learn how to interpret and report the results of these analyses." . . "Presential"@en . "TRUE" . . "Dissertation"@en . . "60" . "he dissertation is an independent piece of scholarship linked to an applied research topic and is designed to complement and utilise the skills developed across taught modules. On successful completion of the module, you should be able to:\n \n plan and design a suitable programme of data collection Effectively collect data in the field or through collation of secondary data;\n present and analyse your results clearly and accurately;\n draw conclusions and to identify management implications;\n write concisely and lucidly both in styles appropriate to a scientific report and accessible to the lay person." . . "Presential"@en . "TRUE" . . "Analysis of environmental data"@en . . "20" . "The ability to analyse complex data is a vital skill in a scientist's toolbox, whether working with experimental or observational data. This module introduces data analysis in the framework of linear modelling using the open-access R software." . . "Presential"@en . "FALSE" . . "Field techniques"@en . . "20" . "It provides a knowledge of environmental monitoring techniques and their limitations an understanding of survey techniques and sampling issues an awareness of up to date technologies available for fieldwork. On successful completion of the module, you should be able to:\n \n critically assess the appropriateness of different field techniques for a range of contexts;\n evaluate sources of uncertainty in field measurements;\n instruct novices in the use of a specific field technique;\n plan and design an appropriate field-based sampling programme." . . "Presential"@en . "FALSE" . . "Commercial and scientific applications"@en . . "20" . "Data Science is a rapidly emerging discipline at the intersection of computer science, statistics, and application domains. The main goal of data science is to extract knowledge and insight from data, which can then be turned into positive action. \n \n This module introduces the fields of Data Science and Big Data. It covers the fundamental steps of the data science process, as well as some specific techniques and case studies. Guest speakers from industry and academia will appear to discuss their data science applications. \n \n There are no programming labs or hands-on data analysis in this module. This is left to other modules in the programme. The idea is instead to provide a high-level discussion of important principles and study the conceptual steps of the data science process. The module is assessed by a critical essay of a specific data science case study, from a recent journal article, selected by the student from a given preselected set of articles. n successful completion of the module, you should be able to:\n \n have a critical knowledge of the fields of data science and big data, including an understanding of the skills required from a data scientist;\n develop a critical understanding of the key stages of a data science project, and apply this knowledge while critically analysing a real-world data science case study;\n develop a critical awareness of current issues in real-world practical commercial and scientific applications of data science;\n communicate, using writing, oral and visual methods, the development and findings of a real-worlOd successful application of data science." . . "Presential"@en . "FALSE" . . "Master in Environmental Remote Sensing Geospatial Sciences"@en . . "https://www.stir.ac.uk/courses/pg-taught/environmental-remote-sensing-geospatial-sciences/#panel_modules_002" . "160"^^ . "Presential"@en . "This course provides a thorough scientific grounding in remote sensing \nand professional training in geospatial technology and programming, with\n a unique focus on environmental applications and space exploration. The MSc Environmental Remote Sensing and Geospatial Sciences aims to prepare graduates for a successful career in:\n\n environmental, heritage and resource management (e.g. agriculture, fisheries, energy, insurances, waste crime) sectors\n the fast-growing downstream space and technology-driven industries, including government regulators, local authorities, universities and space agencies\n\nWe aim to provide an in-depth, critical and balanced understanding of the theoretical foundations of remote sensing alongside current and emerging environmental and other applications.\n\nThis course will develop transferable professional and academic skills and will combine interdisciplinary scientific knowledge in environmental sciences with practical and cutting edge observation technology."@en . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "10350.00" . "British Pound"@en . "21845.00" . "None" . "This MSc course includes work-related assessments and guest speakers from \npublic, private and third sector. Collectively, these will help you to \nset your career expectations and provide you with knowledge and skills \nwhich are highly relevant to recruitment needs.\nThe modules also integrate career readiness skills through the use \ncase studies from different sector perspectives. This gives you the \nopportunity to familiarise with real-life solutions to common challenges\n in Earth observation and space exploration."@en . "1"^^ . "FALSE" . "Downstream"@en . . . . . . . . . . "Biological and Environmental Sciences"@en . .