Master of Data Science (Earth and Environment)
Gain the key skills and knowledge needed to address the challenges of utilising the streams of data produced in modern industry, science and government.
1 year full-time
As part of a suite of Data Science Masters this course is targeted at Geographers, Earth and Environmental Scientists who want to learn how to work with managing natural resources and spatio-temporal information flows. These courses are designed to equip students with the key skills and interdisciplinary knowledge to address the challenges of utilising the streams of data produced in modern industry, science and government.
The advantage of taking one of these Masters at Durham is that you will be part of a cohort of Masters students who will work together throughout the 12 month course. Included with the Masters course your research project could be carried out in conjunction with a range of partners across industry, regulators and policy-makers building on Durham’s significant strengths in delivering world-leading and world changing research.
To equip you for your career all of the students on the suite of Data Science Masters courses share core modules. These shared modules mean you will be carrying out team building activities, present case studies and carry out both formative and summative assessments with students from all four faculties of Durham University ensuring that you learn how to represent not just your own discipline but listen and the integrate views and skills from other disciplines.
All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Business, research, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for any one individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.
Previously, data science was the province of experts in maths and computer science, but the advent of new techniques and increases in computing power mean that it is now viable for non-experts to learn how to access, clean, analyse, and visualize complex data. There is thus a growing opportunity for those already in possession of knowledge about a particular subject or discipline, and who are therefore able to grasp the full meaning and significance of data in their area, to be able to undertake data analysis intelligently themselves. The combination of primary domain knowledge with an expertise in extracting relevant information from data will give those with this ‘double-threat’ a significant employment advantage.
The Master of Data Science programme is a course with a hard-core of data science, intended to provide Masters-level education rich in the substance of data science for students who hold a first degree that is not highly quantitative, including those in social sciences, the arts and humanities. Introductory modules are designed to bring students with non-technical degrees up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce students to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow students to focus on an area of interest.
The programme provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:
The programme is designed around a pedagogical framework which reflects the core categories of the data science discipline.
A number of subjects can be identified and defined within each application domain. Whilst a Masters programme cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the programme incorporates the necessary breadth and depth of material to ensure a skilled graduate.
The programme allows for progressive deepening in the students’ knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.
The global dimension is reinforced through the use of international examples and case studies where appropriate.
Text Mining and Language Analytics
Data Exploration, Visualization, and Unsupervised Learning
The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science (www.durham.ac.uk/idas/people).
Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments. They are also developed through innovative teaching practices such as simulations. Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.
All modules taught on this programme are underpinned by research, and embed elements of research training both in the delivery and in the assessment.
The Master of Data Science uses a wide range of learning and teaching methods:
The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.
The course will be delivered by a combination of lectures, practicals, workshops and coding surgeries. The course will include a data camp where data is collected using hierarchies of techniques and processed across a range of scales.
A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences. Candidates with a degree in Geography, Earth or Environmental Sciences are strongly encouraged to apply.
Evidence of competence in written and spoken English if the applicant’s first language is not English:
|Home students||£12,900 per year|
|EU students||£29,500 per year|
|Island students||£12,900 per year|
|International students||£29,500 per year|
The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).
Please also check costs for colleges and accommodation.
We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities.Find out more about Scholarships and Bursaries
Natural Sciences is the Faculty of Science “Department” that facilitates multidisciplinary and interdisciplinary degrees.
All around us, massive amounts of increasingly complex data are being generated and collected and business, research, government, communities, and families can use that data to make informed andrational decisions that lead to better outcomes. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.
For more information see our department pages.
The Master of Data Sciences degree programmes combines material from across departments. The email contact is firstname.lastname@example.org
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