The objective of the course is to supply students with practical routes to establish and communicate meaningful outcomes from ‘big data’ to the user communities served, using the latest generation of information manipulation and visualisation techniques. The course represents an exciting combination of rigorous academic, technical and practical training, providing a thorough training in the technical, analytical and research skills needed for a career in this expanding field. The Environmental Data Science course will supply graduates with the practical skills and capabilities necessary to manage and manipulate such ‘big data’ to provide effective information tailored to the management of particular environmental systems.
Environmental Data Science is developing into an increasingly important professional specialism, where large datasets of spatial and temporal information are assembled and manipulated in order to improve the understanding and management of complex environmental systems. However, the sheer volumes of data collected, often termed ‘big data’, challenge traditional methods for structuring, manipulating and outputting information for decision makers. Such data is gathered by modern real-time sensors and data loggers, satellite and aerial remote observation platforms, machinery, and simulation outputs such as climate-change modelling applications. The Environmental Data Science course will supply graduates with the practical skills and capabilities necessary to manage and manipulate such ‘big data’ to provide effective information tailored to the management of particular environmental systems.
The course comprises eight assessed modules, a group project and an individual research project.
The early part of the Environmental Data Science course is structured around a series of taught modules consisting of lectures, tutorials, demonstrations and practical classes taken during the autumn and spring. Each module forms the sole unit of study for a period of two weeks. An opportunity to undertake a project in the style of a consultancy project is offered on the full time programme and is conducted from mid February to April. The period May to August is devoted to an individual thesis project. Additionally, the taught component of the programme is supported by visits and seminars from and to industry practitioners. The individual modules are linked through case studies and practical work so that the different aspects of the technologies utilised are integrated.
The Group Project experience follows the taught components and is highly valued by both students and prospective employers. It provides students with the opportunity to take responsibility for a consultancy-type project whilst working under academic supervision and with others in a team environment.
The project typically involves the application and integration of component technologies, including for example:
- Big data analytics and data mining
- Web services and tools
- Software engineering
- Data visualisation
- Statistical analysis and interpretation.
The Group Project work is designed to produce quality-assured innovative solutions of direct value in seeking future employment.
The Individual Thesis Project may be either industrially or Cranfield University
driven. Students select the individual project in consultation with the course team.
Such project work enables the knowledge and skills gained from the taught element of the programme to be put into real-world practice and, gaining transferable skills in project management, team-work and independent research. Future employers value this experience. Students will have exciting opportunities to conduct project work with support from industry and other external organisations with academic supervision. Future employers value this experience highly.
The project provides students with the opportunity to demonstrate independent research ability, the ability to think and work in an original way, contribute to knowledge, and overcome genuine problems in relation to the management of the earth's resources. It also offers students the opportunity to work with the types of organisation they will be seeking employment with on successful completion of the course.
For part-time students, the research project is usually performed at their employer's premises on a topic of interest to the student and the employer.
Project topics may draw on the following themes:
- interpretation of dynamic geospatial modelling and simulation outputs
- application of multi-temporal techniques to explore prevailing, historical and predicted environments
- emergent data visualisation and presentation techniques
- the application of web-based, service oriented computing architectures to convey and represent spatial information
- the development of simulation modelling frameworks for representing complex environmental systems.
This course comprises eight taught modules, a group project and an individual project.
Environmental Risks - Hazard, Assessment and Management
Spatial Data Management
Modelling Environmental Processes
Applied Environmental Informatics
Programming with Java
Environmental Resource Survey
Spatial Data and the Internet
Taught modules MSc 40%, PgDip 66.6%. Group project (dissertation for part-time students) MSc 20%, PgDip 33.3%. Individual project MSc 40%.
Funding opportunities exist, including industrial sponsorship, School bursaries and a number of general external schemes. For the majority of part-time students sponsorship is organised by their employers. We recommend you discuss this with your company in the first instance.
The UK has one of the world's strongest digital markets and data, in all its forms, is now so important in organisations that analysts rate it as a major competitive advantage (The Independent, Nov 2011). The ICT, software and digital content sectors are together worth £100bn. The UK digital economy is estimated to be larger per head than in any other country and it is expected to grow to 10% of GDP by 2015 (Technology Strategy Board).
In Europe as a whole, ‘Big Data’ is estimated to generate significant financial value to the tune of EUR250bn per year across the public sector (McKinsey Global Institute). In the UK, there are estimates in which the digital economy accounts for nearly £1 in every £10 that the UK economy produces each year (Dept. for Culture, Media and Sport). There are clear government efforts (eg. cross-research council ‘Digital Economy’ or Technology Strategy board ‘Connected Digital Economy Catapult’ to promote and support the digital economy).
Sustainable development is one of the key themes noted in many of the strategy statements and growth outcomes. In these same studies (and others), key skills required are those associated with Modelling, Multi-disciplinarity, Data Management and Numeracy (ERFF). Stated more simply by the UK Department for Business, Innovation and Skills (BIS), this equates to the fields of science, technology, engineering and mathematics.
The Digital Economy will require graduates with the technical skills to manage, manipulate and visualize large datasets (IT technology and engineering) and interpret and represent this data as information and knowledge (science and mathematics). However, currently a ‘significant constraint on realising value from big data will be a shortage of talent’ (McKinsey Global Institute).
Cranfield graduates from this course can expect to follow a wide-range of career paths in academic research or professional environmental management; environmental consultancy with private firms, non-profit organizations and government. Environmental Data Science will furthermore provide for an enhancement of skill sets for mid-career professionals as it represents an emerging set of quantitative tools which achieve concrete solutions to trans-disciplinary problems.
For further information on this course, please visit our course webpage - http://www.cranfield.ac.uk/Courses/Masters/Environmental-Data-Science
A first or second class UK Honours degree in a relevant science, engineering or related discipline, or the international equivalent of these UK qualifications. Other relevant qualifications, together with significant experience, may be considered.