Page 3 of 69, 1031 Course Programs in United Kingdom for 2024

This course is specifically tailored for biologists who want to apply machine learning and mathematical modelling on life science data. You will learn the fundamentals of machine learning and also acquire practical experience on a wide variety of techniques for regression and classification modelling using the R environment. Machine learning is a rapidly expanding form of artificial intelligence (AI) which has found many applications in the field of Biosciences. Delivered by world-leading expert data scientists, course participants will have the opportunity to learn the fundamentals of machine learning using a practical hands-on approach. We will be covering generalised linear regression, penalised regression, decision trees and random forests, support vector machines, and artificial neural networks. Through a series of practical sessions, you will have the opportunity to use a range of biological datasets from different omics platforms to develop and validate prediction models for classification and numeric prediction purposes. All practical sessions will be delivered using the R environment.

A basis and understanding of methods pertaining to Informatics is needed to effectively obtain information from data. The objective of this course is to supply the student with a toolbox of techniques for data mining and modelling (informatics) and develop in the student the strategic ability to effectively apply this toolbox. Assemble and organize data for prescribed analysis and modelling approaches.

Environmental resource surveys are required to obtain the data used in environmental information management. This course will identify the objective of a survey of the environment and determine the appropriate survey method to undertake an assessment of environmental resources. Determine the appropriate survey method to undertake an assessment of environmental resources.

This course introduces techniques for the extraction of topographic information from remotely sensed data using softcopy photogrammetry techniques. Deriving digital elevation models and ortho imagery is an important application of remote sensing data. Image interpretation is a vital skill required in many image based mapping projects. The concepts and techniques of image interpretation will be introduced and practised.

This short course will provide information about the importance of individual and group attitudes towards the perception of risk and how this may influence views, conduct and actions in the face of a range of risks including the development of communication methods to disseminate information about risk(s) to a range of audiences and how to determine its effectiveness.

The purpose of this course is to introduce you to the practical issues associated with implementing Environmental Management Systems (EMS) into organisations. This requires a thorough understanding of the concept of an EMS, as well as the most commonly used frameworks, primarily the ISO 14000 series of standards. In addition, knowledge of the potential barriers and pitfalls during implementation and the practical measures that can be implemented is provided.

A critical application of environmental risk management is in the development and appraisal of policy in central government and business. Policies are developed to manage environmental risks and selection of policy options must be informed by risk based tools and techniques. Doing so demands a comprehension of the technical, organisational and human elements of governing environmental risks and developing environmental policy. This course draws these themes together by introducing core concepts and then illustrating these concepts with case studies and finally application via a group exercise.

While technological change is seen as the root cause of many environmental problems, it is simultaneously viewed as the means of solving such problems. This course explores technological change as part of positive sum strategies put forward by ecological modernisers. Theories of technological change are reviewed such as evolutionary, path dependent and long wave and used to formulate technology policy to achieve environmental net gain or transition to a low carbon economy. These are then set in practical contexts such as innovation in manufacturing, ecological restoration or low-carbon living.

Interested in learning how to manipulate Earth Observation data? The Applied Remote Sensing Group at Cranfield University will be hosting a training short course in Earth Observation (EO) data science. The course will equip you with the skills to effectively extract, manipulate, and visualise EO data for a variety of use cases. Highly rated (4.65/5) by past participants for its practical approach and beneficial impact on research and professional development, this course is an invaluable opportunity to advance your skills in the growing field of EO data science.

Strategic foresight research refers to a range of methods that can be used to identify, analyse and communicate insights about the future. Standard methods include horizon scanning, trend research, and scenario planning. Outputs include emerging issues, trends, visions, scenarios, and wild cards. The methods employed and insights produced are used by both private and public sector organisations to inform a wide range of policy, risk, strategy and innovation processes. Foresight research is a truly inter-disciplinary ‘science’, covering and combining developments in society, technology, economy, ecology, politics, legislation and values.

Water is an essential factor of production in agrifood systems; whether for growing crops, supporting livestock or food manufacture. Globally, 70% of freshwater withdrawals are used for agriculture, but increasing demand for food means that this figure is likely to increase dramatically in the future. At the same time climate change is affecting supply and other demands on water are increasing. Mismanagement of water for food production has led to social and environmental problems in many places. Water is therefore a significant global risk to sustainable food production. This module will consider the water requirements of crop and livestock systems; the evaluation of the water related impacts and risks in producing locations; and management and technological solutions to minimise water related impacts and risks in food supply chains.

This course provides a critical appraisal of the role of the main plant-based technologies which can be used to advance sustainable crop production and food security. This includes a consideration of the importance of crop breeding, seed technology and crop protection with particular emphasis on future needs. Explain the main strategies and technologies in producing new, improved varieties of crop plants.

The purpose of this course is to provide a set of practical applications and tools for developing, managing and analysing ‘Big Data’, to better deliver food security. A secure, reliable and sustainable food production system will increasingly rely on advanced technologies, such as real-time field sensing, model data fusion and advanced forecasting. It will need to operate effectively within new and changing environmental constraints and so will need to consider and be represented within (eco) systems goods and services models to ensure food security that is both economic and environmentally sustainable. This course will introduce and develop core skills in data acquisition, data and information management, using numerical and statistical modelling approaches that form the basis of information driven sustainable agriculture. It will incorporate ground, aerial and space borne sensing and sensor techniques for predictive mapping within the context of modelling agricultural ecosystems goods and services.

This course will focus on a fundamental understanding of the science of soil systems and how decisions in land management affect the soil functions related to food production. Food security, environmental protection and mitigation of climate change impacts depend upon effective management of soil, plant and water interactions in the environment.

Human population growth and increased resource use per capita requires improved management of our global ecosystem. Approaches such as the “Sustainable Development Indicators”, “Natural Capital”, “Ecosystem Services”, and “Planetary boundaries” provide frameworks for businesses and wider society to resolve the synergies and trade-offs between major economic, environmental and social challenges.