Master of Data Science (Earth and Environment)

Program Overview

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Master of Data Science (Earth and Environment)

Gain the skills and knowledge to harness the fast-growing streams of environmental data produced by industry, science and government. The course examines the data science techniques being used to address key environmental issues.

Start Dates

• September 2025

Degree Type

• MDS

Course Length

• 1 year full-time

Location

• Durham City

Programme Code

• G5P123

Course Details

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. 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 in which you apply the skills learned during the course to a research problem working alongside an expert in the area of application of your choice. There may be an option to carry out the project in conjunction with an industry partner.

Course Structure

Year 1 Modules

Core Modules:

• Data Science Research Project: 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 Analysis in Space and Time: 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 using the R statistical language. 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, and neural networks.

Optional Modules:

• 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 Science

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 and computer/practical classes. The taught elements are further reinforced through independent study, group work, research and analysis, case studies and structured reading.

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 IELTS requirement is 6.5 overall, with no component under 6.0
  • Minimum TOEFL requirement is 92 overall, with no component under 23

Alternative Qualifications

• Other UK qualifications
• EU qualifications
• International qualifications

Fees and Funding

Full Time Fees

• Tuition fees:
  • Home students: £14,500 per year
  • EU students: £34,000 per year
  • Island students: £14,500 per year
  • International students: £34,000 per year

Career Opportunities

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

Our Masters of Data Science programmes deliver the expertise to capture and analyse the huge amounts of data being generated across the world. The suite of interdisciplinary degrees unlocks key information that will ultimately result in more informed decisions being made in all areas of life.

The Masters of Data Science programmes developing expertise on the capture 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 such as health, environmental sciences, and social analytics.

In an academic context, data science has a key role in underpinning research activity around many subject specialisms. Our Master of Data Science degrees are offered as conversion courses for those who hold a first degree that doesn’t have a strong component in data science.

Seven 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, Social Analytics, and Heritage.

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.

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.

Program Outline

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Degree Overview:

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Master of Data Science (Earth and Environment)

This conversion course equips graduates with the skills necessary to access, clean, analyze, and visualize data, opening up career opportunities in data science even for those without a quantitative background. The program focuses on the practical applications of data science in managing natural resources and spatio-temporal information flows, particularly appealing to geographers, earth, and environmental scientists.

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Objectives:

• Equip students with the skills to handle and analyze diverse environmental datasets from various sources across various spatial and temporal scales.
• Train students in contemporary data science techniques, including neural networks, analysis of spatial and temporal datasets, and deep learning.
• Develop students' quantitative skills for applying data and modeling analysis in the field of natural resources.

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Description:

The Master of Data Science (Earth and Environment) draws upon expertise from across the University, providing students with a comprehensive understanding of data science in the context of earth and environmental sciences. Through a combination of lectures, seminars, workshops, practical classes, and independent research, students develop the skills and knowledge necessary to tackle real-world challenges in environmental management and resource utilization. This interdisciplinary approach ensures graduates are well-equipped to contribute to a data-driven future in this field.

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Outline:

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Program Content:

The Master of Data Science (Earth and Environment) consists of core modules covering fundamental data science skills and specialized modules focusing on their application in earth and environmental sciences. Students also undertake a research project, applying their learned skills to a specific area of interest related to the field.

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Structure:

• Core Modules:
• Introduction to Statistics for Data Science
• Machine Learning
• Programming for Data Science
• Data Science Tools in Earth Sciences
• Data Science Applications in Earth Sciences
• Specialist Modules (choose two):
• 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
• Research Project

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Course Schedule:

The program runs for one year, starting in September 2024. The course schedule includes lectures, seminars, workshops, and independent study time.

• Machine Learning: Introduces the key knowledge and skills required for machine learning in data science, covering topics such as regression methods, decision-based learning techniques, support vector machines, neural networks, and deep learning.
• Programming for Data Science: Teaches how to gather, manipulate, and process real-world data using popular Python software packages, emphasizing key concepts of data analysis and visualization.
• Data Science Tools in Earth Sciences: Provides understanding of data methods and tools used in earth and environmental sciences, with a focus on analyzing spatial and temporal datasets.
Introduces physical modeling of complex real-world systems and popular industry software packages.
• Data Science Applications in Earth Sciences: Offers hands-on experience in handling, amalgamating, and analyzing diverse earth and environmental datasets from various sources and across spatial and temporal scales.
Uses datasets to address prominent issues in earth and environmental sciences, exploring and applying popular industry software packages.

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Assessment:

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Assessment Methods:

• Essays
• Online assessments
• Reports
• Presentations
• Research Project

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Assessment Criteria:

• Understanding of key concepts and theories
• Application of data science methods and techniques
• Ability to analyze and interpret data
• Communication and presentation skills
• Research skills

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Teaching:

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Teaching Methods:

• Lectures
• Seminars
• Workshops
• Practical classes
• Coding surgeries
• Data camps
• Independent study
• Research
• Analysis
• Case studies

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Faculty:

The program is delivered by academic staff from various departments across the University, including Computer Science, Mathematics, Geography, and Earth Sciences. The teaching team comprises active researchers who bring real-world experience and expertise to the curriculum.

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Unique Approaches:

• Interdisciplinary curriculum combining data science with earth and environmental sciences.
• Emphasis on practical applications and problem-solving.
• Data camp experience for immersive data collection and analysis.
• Research-led teaching and assessment, encouraging critical thinking and research skills development.

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Careers:

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Career Paths:

• Data scientist
• Environmental consultant
• Spatial analyst
• Resource management specialist
• GIS analyst
• Research scientist

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Career Opportunities:

Graduates from the Master of Data Science (Earth and Environment) will be well-positioned for careers in various sectors, including government agencies, environmental consultancies, research institutions, and private companies engaged in data-driven resource management and environmental analysis. The program's interdisciplinary nature opens up possibilities across diverse fields, allowing graduates to apply their data science expertise to tackle real-world challenges in earth and environmental sciences.

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Career Outcomes:

The program aims to equip graduates with the skills and knowledge to pursue successful careers in data science and related fields within the earth and environmental sciences sector. The program's strong focus on practical applications and industry-relevant software prepares students for immediate contributions in data-driven organizations, leading to potential leadership positions and continued growth within their chosen career paths.

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Other:

• The program is suitable for graduates from any degree, regardless of their quantitative background.
• The program is based in the historic city of Durham, a UNESCO World Heritage site.

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Full Time Fees

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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).