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MSc PG Dip PG Cert Data Science for Global Agriculture, Food and Environment

  • DeadlineStudy Details:

    MSc/PGDip/PGCert 1 year full-time, up to 3 years part-time

Course Description

There is a huge skills gap in the UK workforce when it comes to data science and artificial intelligence. The Government’s Digital Skills strategy estimates that within the next 20 years, 90 per cent of all jobs will require some element of digital skills, with data science pinpointed as a priority area.

At the same time, the agriculture, food and environment sectors are experiencing a radical shift in demand for data scientists, thanks to applications in agri-tech, and smart farming and a large surge in demand for general skills using big data and open data across the sector through 2030. At the same time, there is a huge demand for data-driven solutions for best-practice solutions for conservation and environment issues that are compatible with the future of farming. This new course, the first and only of its kind in the UK, seeks to address these challenges.

Entry Requirements

This course is ideal for students with a background in agriculture who are interested in pursuing a career in data science, or for students with a background in computing or maths interested in pursuing a career in data science in the agriculture and food industries.

Candidates should possess one of the following:

  • An honours degree in agriculture, environmental science, or related scientific subject.
  • A good UK based Higher National Diploma or Foundation Degree or equivalent in agriculture, environmental science, or related scientific subject together with related industrial or professional experience of at least two years.
  • A Graduate Diploma, Graduate Certificate or equivalent.

To apply for this course a degree indicating basic quantitative and mathematical skills is required and applicants are expected to demonstrate some ability and interest in this area. Whilst formal techniques are taught as part of the MSc course, some prior training and enthusiasm in these areas is expected.

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Fees

For fees and funding options, please visit website to find out more.

Programme Funding

There are many sources of financial assistance available, please visit website for details.

Student Destinations

This course is ideal for candidates with a background in agriculture, food science, or in wildlife, conservation and environmental science, or for someone from a data science background that wishes to enter one of these subject areas. Our flexible programme consists of core training in data science tools and further specialised training, so that you can choose your own path:  help agri-food companies make smarter business decisions, or use data to solve important conservation challenges.

Module Details

MSc 180 credits from:

  • Statistical Analysis for Data Science
  • Techniques in Machine Learning and Artificial Intelligence
  • Data Visualisation and Analytics
  • Elective module
  • Big Data and Decision Making- Case Studies
  • Masters Research Project

Optional Modules

  • Geographical Information Systems
  • Fundamentals of Agroecology
  • Ecological Entomology
  • Biodiversity and Ecosystem Services
  • Food Sustainability and Ethics
  • Agri-food Supply Chain Strategy, Operations and Management
  • Agricultural Economics, Policy and Trade
  • Elective module

PGDip 120 credits from:

  • Statistical Analysis for Data Science
  • Techniques in Machine Learning and Artificial Intelligence
  • Data Visualisation and Analytics
  • Elective module
  • Big Data and Decision Making- Case Studies

Optional Modules

  • Geographical Information Systems
  • Fundamentals of Agroecology
  • Ecological Entomology
  • Biodiversity and Ecosystem Services
  • Food Sustainability and Ethics
  • Agri-food Supply Chain Strategy, Operations and Management
  • Agricultural Economics, Policy and Trade
  • Elective module
  • Experimental Design and Analysis

PGCert 60 credits from:

  • Statistical Analysis for Data Science
  • Techniques in Machine Learning and Artificial Intelligence
  • Data Visualisation and Analytics
  • Experimental Design and Analysis

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