Environmental Data Science and Machine Learning MSc
Imperial College London, South Kensington Campus, United Kingdom
Environmental Data Science and Machine Learning MSc
Imperial College London
Environmental Science & Data Science
Program Overview
The Environmental Data Science and Machine Learning MSc at Imperial College London trains future environmental scientists in data science, machine learning, and associated computational technologies. You will deepen your knowledge of cutting-edge techniques and learn to apply them to a broad range of environmentally motivated applications, from climate modelling to renewable energy systems.
The programme explores how data science techniques can be used to develop solutions to real-world environmental challenges. You will become familiar with key aspects including cloud computing, remote sensing, environmental monitoring, modelling, and computer code, building a versatile and highly sought-after skill set.
A substantial research project is a key component of this degree, where you will contribute to an active research area and develop your critical analysis skills. This programme is part of the Ada Lovelace Academy, an initiative from the Department of Earth Science and Engineering that aims to deliver gender-balanced postgraduate education in computational subjects.
Key Program Highlights
- Part of the Ada Lovelace Academy promoting gender-balanced STEM postgraduate education at Imperial College London
- Hands-on research project contributing to active environmental science research areas within the department
- Comprehensive training in machine learning, cloud computing, remote sensing, and environmental monitoring techniques
- Skills applicable across all of science and engineering, opening hugely diverse career paths in environmental and tech sectors
Curriculum & Modules
The curriculum is structured to provide a robust foundation in both theoretical and practical aspects of environmental data science and machine learning. Core modules cover essential mathematics, programming, data analytics, deep learning, and environmental data processing, culminating in an individual research project.
Explore the essential mathematics underpinning computational science, data science, and machine learning, including linear algebra, calculus, and probability theory.
Discover how to build neural networks to recognise images and language, generate new content, and make predictions by learning complex patterns from data.
Obtain the core knowledge and skills required for processing and analysing data in the context of environmental science, including methods for handling incomplete and inconsistent datasets.
Master compiled programming in C++, including memory management, object-oriented design, and performance optimisation for building efficient and robust software.
Acquire the skills, knowledge, and methods necessary to perform scalable data analytics in different situations, including cloud computing and large-scale data processing.
Elective & Specialisation Options
Admission Requirements
Imperial College London considers all applicants on an individual basis, welcoming students from all over the world. Admission to this programme is competitive, and candidates are assessed on their academic qualifications, relevant experience, and English language proficiency. All applications are processed through Uni4Edu.
Academic Requirements
- Minimum Degree Classification2:1 (Upper Second Class Honours) or international equivalent
- Discipline BackgroundEngineering or science-based discipline
- Mathematics RequirementStrong background in mathematics including algebra and multivariate calculus
- International QualificationsA wide variety of international qualifications are accepted; contact Uni4Edu for equivalency guidance
- Additional ConsiderationsRelevant work experience and academic references may strengthen your application
English Language Requirements
- IELTS (Academic)Minimum 6.5 overall with minimum 6.0 in each component (standard requirement)
- TOEFL iBTMinimum 92 overall with minimum scores in each section
- PTE AcademicMinimum 62 overall with consistent scores across sections
Required Documents
Application Deadlines
For personalized admission guidance, document verification, and application support, please contact Uni4Edu
Scholarships & Funding
Imperial College London and external bodies offer a range of scholarships and funding opportunities for postgraduate students. Uni4Edu can help you identify and apply for funding that matches your profile and financial needs.
President's PhD Scholarships (for progression)
Imperial offers prestigious scholarships for outstanding students who wish to continue to doctoral study. Eligibility and coverage vary by year and are highly competitive.
Ada Lovelace Academy Scholarship
Full home tuition fee coverageA scholarship for exceptional UK-eligible students on the Environmental Data Science and Machine Learning MSc who demonstrate community engagement and social impact potential.
Imperial College Trust Bursaries and External Funding
Varies by awardA range of bursaries and external scholarships are available for international and home students. Contact Uni4Edu for guidance on eligibility and application processes for your specific circumstances.
For detailed tuition fee information, please contact Uni4Edu — we will guide you through the costs and available funding options for this program.
Career Prospects
Graduates of this programme are equipped with skills that meet significant market demand for applied, hands-on computational and data science expertise. The combination of environmental science knowledge and advanced data science capabilities makes graduates attractive to a wide range of employers, from small consultancies to large multinational organisations.
Potential Career Roles
Typical Employers
Rankings & Recognition
Imperial College London is consistently ranked among the top universities in the world, with particular strength in science, engineering, and technology disciplines. The Department of Earth Science and Engineering benefits from the university's outstanding research reputation and global employer recognition.
| Subject | Ranking Body | Rank |
|---|---|---|
| Engineering & Technology | QS World University Rankings | Top 10 |
| Earth & Marine Sciences | The Guardian University Guide | Top 10 in UK |
| Computer Science & Information Systems | QS World University Rankings | Top 15 |
| Environmental Sciences | QS World University Rankings | Top 20 |
How to Apply
Applying for this program is easy with Uni4Edu. Our team will guide you through every step of the process — from document preparation to final enrolment.
Contact Uni4Edu
Reach out to our team via email or phone. We will assess your profile and confirm your eligibility for this program.
Prepare Your Documents
Our advisors will provide you with a personalised checklist of required documents and help you prepare your application package.
Submit Your Application
Uni4Edu will submit your application on your behalf and keep you updated on its progress throughout the review period.
Receive Your Offer
Once accepted, we will help you understand your offer, arrange visa support if needed, and guide you through the enrolment process.
Similar Programmes
Bachelor's Degree
48 months
Environmental Science
Manhattan University, Bronx, United States
Earliest Intake
June 2025
Gross Tuition
44100 $
Master's & Postgraduate
12 months
Climate Change, Management and Finance MSc
Imperial College London, , United Kingdom
Earliest Intake
January 2026
Gross Tuition
45500 £
Master's & Postgraduate
12 months
Conservation and Land Management
Prifysgol Bangor University, Bangor, United Kingdom
Earliest Intake
May 2026
Gross Tuition
19500 £
Master's & Postgraduate
12 months
Engineering Fluid Mechanics for the Offshore, Coastal and Built Environments MSc
Imperial College London, , United Kingdom
Earliest Intake
January 2026
Gross Tuition
45000 £
Bachelor's Degree
48 months
BSc (Hons) Environmental Studies
San Francisco State University, San Francisco, United States
Earliest Intake
October 2025
Gross Tuition
16400 $
Uni4Edu AI Assistant
