Program Overview

Environmental Data Science MSc, PGDip

Course Description

The MSc in Environmental Data Science is an interdisciplinary program that equips students with the skills to manipulate, analyze, model, and visualize environmental data. The program addresses the shortage of qualified data scientists in the UK and abroad, providing students with a broad range of fundamental data science techniques.

Entry Requirements

• Students are required to have a second-class honors degree in any subject.
• Students with non-standard qualifications are encouraged to apply.
• Prior professional experience gained by mature students in relevant areas of work will be considered.
• IELTS 6.5 or equivalent is required for international students.

Fees and Funding

• UK Students:
  • MSc: £10,950
  • MSc with Industry: £10,950
  • PGDip: £7,295
• International Students:
  • MSc: £23,650
  • MSc with Industry: £23,650
  • PGDip: £15,750
• An additional fee of £3,520 will be charged if a placement is secured.

Careers and Employability

The program furnishes students with technical and transferable skills, enabling them to pursue careers as Environmental Data Scientists/Analysts/Consultants in various businesses, consultancies, governments, and research institutions.

Related Courses

• Environmental Futures MSc
• Geographical Data Science MSc
• Satellite Data Science MSc

Course Structure

Core Modules

• Introduction to Environmental Modelling
• Overview of Data Science Practice
• Statistics for Data Science
• Fundamentals of Data Science
• Applications of Environmental Modelling
• Research for Change: Skills and Challenges of Applied Environmental Research
• Dissertation

Option Modules

• Geospatial Data Analytics
• Geographical Artificial Intelligence
• Field Data Capture
• Information Visualisation
• Overview of Data Science Practice

'With Industry' Option

The 'With Industry' variant of this degree gives students the opportunity to take an Industrial Placement as part of their Masters. This is a work placement with an employer where students develop their skills in a 'real-world' setting.

Teaching and Learning

The program is taught using a range of methods, including lectures, seminars, practical classes, workshops, tutorials, project supervision, and fieldwork. The program includes a blend of assessment, including traditional and authentic assessment types.

Why Leicester?

The program is accessible to students without a strong computing or mathematical background. Transferable data science skills are explicitly taught and developed within strong disciplinary and vocational contexts. The program is taught by staff with international reputations as researchers and consultants to businesses and government agencies. NA