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Start Dates
Degree type

MDS

Course length

1 year full-time

Location

Durham City

Program code

G5P123

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Course details

From security cameras and satellite transmission to our mobile devices, homes, cars, and in the workplace, we are surrounded by data. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment. This has led to a significant increase in demand for skilled data scientists, and this demand is predicted to further grow.

Drawing on this, we have created the Master of Data Science (Earth and Environment), a conversion course that equips you with the skills to access, clean, analyse and visualise data, opening a future in data science even if your first degree is in a non-quantitative subject. It is likely to appeal to geographers, earth and environmental scientists who want to learn how to use the data produced in modern industry, science and government in the management of natural resources and spatio-temporal information flows.

The course provides training in contemporary data science. You will be based in a supportive environment, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses will equip you with wider statistical and machine learning skills, while subject-specific earth and environment modules develop your quantitative skills in the field of natural resources. It is equally suitable whether you are planning to use quantitative analysis in a research capacity, or if you are a geography or environmental graduate who wants to learn transferable data and modelling analysis skills.

The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as neural networks, analysis of spatial and temporal datasets and deep learning. Optional modules allow you to focus on an area of interest.

The MDS culminates in the research project, an in-depth investigation into an area of specific interest in which you apply the skills you’ve learned during the course to a research problem related to earth and the environment. There may be an option to carry out the project in conjunction with an industry partner.

Course Structure

The Data Science Research Project is a substantial piece of research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.

Critical Perspectives in Data Science develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project.

Data Science Applications in Earth Sciences provides experience of handling, amalgamating and analysing diverse earth and environmental datasets from a range of sources and across a range of spatial and temporal scales. You will also use datasets to address problems at the forefront of earth and environmental sciences, across a range of topics and explore and use popular software packages currently used in industry settings.

Data Science Tools in Earth Sciences provides an understanding of data methods and tools used in the field of earth and environmental sciences, with a particular focus on those used for analysing spatial and temporal datasets. You will also learn about the physical modelling of complex real-world systems and use popular software packages currently used in industry settings.

Programming for Data Science uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.

Introduction to Statistics for Data Science focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.

Machine Learning introduces the essential knowledge and skills required in machine learning for data science. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, neural networks and deep learning.

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

  • Introduction to Computer Science
  • Introduction to Mathematics for Data Science
  • Text Mining and Language Analytics
  • Data Exploration, Visualisation, and Unsupervised Learning
  • Strategic Leadership
  • Ethics and Bias in Data Analytics

Learning

This interdisciplinary course is made up of modules that span departments across the University. It incorporates a wide range of learning and teaching methods which vary according to the modules studied. These include lectures, seminars, workshops, practical classes and coding surgeries. The taught elements are further reinforced through independent study, research and analysis, case studies and structured reading. The course also includes a data camp where data is collected using hierarchies of techniques and processed across a range of scales.

All modules are underpinned by research and embed elements of research training in both delivery and assessment. Throughout the course you will be encouraged to develop research methods, skills and ethics reflecting the methods used by the research-active staff. Overall, you will be encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for use in future work or research.

Assessment

The Master of Data Science (Earth and Environment) is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups.

The course culminates in a major research project, which is conducted and written up as an independent piece of work with support from your appointed supervisor.

Entry requirements

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:

  • minimum TOEFL requirement is 102 IBT (no element under 23)
  • minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

English language requirements

Fees and funding

Full Time Fees

Tuition fees
Home students £13,500 per year
EU students £31,500 per year
Island students £13,500 per year
International students £31,500 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.

Scholarships and Bursaries

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

Career opportunities

Natural Sciences

The skills and knowledge that constitute a Masters qualification in Data Science are widely sought by employers around the globe. In today’s data-driven society, the ability to capture, analyse and communicate information and trends from the data generated by business, governments and their agencies, communities and organisations is highly prized.

It follows that Data Science is a rapidly expanding career sector with opportunities for stimulating and rewarding work in many sectors including the areas of science, humanities, health, environmental and social where understanding and expertise in data is leading to transformations in the way people live and work.

Department information

Natural Sciences

Postgraduate Natural Sciences provision at Durham is focused data science and its expertise in capturing and processing information derived from the vast volumes of complex data being generated across the globe that affects all our lives.

A wide range of groups such as businesses, researchers, governments, communities, families and individuals can all use that data to make more informed decisions and therefore increase the chances of better outcomes for society, in fields as diverse as health, the environment and social analytics.

In an academic context, data science has a key role in underpinning research activity around many subject specialisms in many disciplines. Our Master of Data Science degrees are offered as conversion courses for those who hold a first degree that is not highly quantitative.

Six qualifications are available including the broad Master of Data Science as well as specialist routes in Bioinformatics and Biological Modelling, Digital Humanities, Earth and Environment, Health and Social Analytics.

Durham University is also home to the specialist Institute for Data Science, which acts as a hub for new ideas and works to realise its vision to help transform nature, society and culture. The Institute has many years of supporting taught degrees from Departments across the University.

Rankings:

  • 2nd in The Times and Sunday Times Good University Guide 2024

Facilities

Data Science is a conversion course that incorporates content from many Departments across the University. This provides access to a selection of related state-of-the-art facilities from across the University, in particular Computer Science and Mathematics.

Facilities will depend on the subject specialism but include laboratories, libraries, project spaces, lecture theatres, study and networking spaces as well as shared social spaces. Most departments are close to the historic centre of Durham which is a UNESCO World Heritage site.

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Find out more:

Apply for a postgraduate course (including PGCE International) via our online portal.  

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  • Date: 01/09/2023 - 31/08/2024
  • Time: 09:00 - 17:00
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  • Time: 09:00 - 16:00
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