Master of Data Science (Earth and Environment)

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

Core modules:

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 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.

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