MSc Applied Data Science (Renewable Energy)

1 Years On Campus Masters Program

University of Exeter

Program Overview

The MSc Applied Data Science (Renewable Energy) at the University of Exeter immerses you in the forefront of the Big Data revolution by combining advanced data science and AI techniques with emerging renewable energy technologies and real‑world applications. It’s perfect for analytical thinkers who want to solve sustainability challenges and gain skills that are increasingly in demand across the renewable energy and data analytics sectors.

Curriculum Structure

Term 1 – Core Foundations
At the start of the programme, you’ll build essential expertise in key areas with modules like Fundamentals of Data Science and Renewable Energy Systems, establishing a strong grounding in statistical, computational and energy principles that will support all your subsequent work. These modules focus on practical numerical methods, data‑handling skills and an introduction to the technologies behind renewable energy.

Term 2 – Advanced Methods and Integration
In the second term you’ll advance into cutting‑edge techniques with Trends in Data Science and AI and Tackling Sustainability Challenges using Data and Models, where you apply what you’ve learned to interdisciplinary problems, combining data science, modelling and real renewable energy challenges. You’ll also have the option to explore specialist topics such as Advanced Wind Energy and Solar Energy Research and Innovation that allow you to focus on specific sectors of the renewable industry.

Term 3 – Capstone Project
Your final term is dedicated to an intensive Data Science and Modelling Dissertation project, where you design and execute your own advanced research or applied data‑modelling investigation under academic supervision. This project is a key opportunity to showcase your interdisciplinary skills in a topic of your choosing, from predictive analytics in energy systems to AI‑driven optimisation for sustainable solutions.

Focus areas:
Applied data science and AI, renewable energy systems, sustainability modelling, advanced computational methods and interdisciplinary analysis.

Learning outcomes:
You’ll graduate with strong analytical and programming proficiency, expertise in managing and modelling complex data sets, an understanding of how to apply AI in renewable energy contexts, and the ability to integrate mathematical models with real‑world sustainability challenges.

Professional alignment (accreditation):
While there isn’t a formal professional accreditation listed for this specific degree, the curriculum is designed in collaboration with research experts and industry partners to prepare you for roles in renewable energy analytics, environmental data science and related sectors where applied mathematical and computational skills are highly valued.

Reputation (employability rankings):
The University of Exeter is consistently ranked Top 20 in the UK for Mathematics (The Times and The Sunday Times Good University Guide), and the interdisciplinary nature of this programme positions graduates well for careers in rapidly growing areas of data analytics and sustainable energy industries

Experiential Learning (Research, Projects, Internships etc.)

In the MSc Applied Data Science (Renewable Energy) you’ll develop highly practical skills that employers in both data science and sustainable energy sectors are actively seeking. The programme is based at Exeter’s Penryn Campus, where you’ll work with modern computational tools used in data science, modelling, and artificial intelligence and apply them directly to real‑world energy challenges. You’ll combine mathematical thinking with hands‑on data analysis and interdisciplinary group problem‑solving not just in the classroom but through project‑based modules and collaborations with researchers from both the Mathematics and Renewable Engineering departments. This means you don’t just learn concepts in isolation: you use them to model systems, interpret data, and propose solutions that matter in the energy transition.

Here’s how experiential learning plays out on this programme:

Practical & Experiential Features of the MSc Applied Data Science (Renewable Energy)

Advanced Project in Data Science & Modelling: In your final term, you’ll complete an in‑depth data science and modelling dissertation — a substantial piece of original work where you choose and explore a real problem using appropriate tools and techniques.
Interdisciplinary Tackling Sustainability Challenges Module: This integrated module brings together methods from data science, AI, and renewable energy to solve real sustainability challenges, often requiring teamwork and cross‑disciplinary thinking.
Core Data & AI Tools: From Fundamentals of Data Science to Trends in Data Science and AI, you’ll work with modern scientific computing and analytical software building fluency in programming and data handling environments that are standard in industry and research.
Renewable Energy Systems Practice: You study real components and systems used in renewable energy research and development, applying data‑driven insights to technologies like wind, marine, or solar energy systems.Optional Specialist Modules: Choose modules such as Advanced Wind Energy, Solar Energy Research and Innovation, or Low Carbon Vehicles to gain focused technical experience and deeper understanding of specific renewable technologies. State‑of‑the‑Art Facilities: The programme makes use of the new Renewable Engineering Facility and associated lab environments at Penryn Campus, giving you access to specialist resources and equipment relevant to energy research and consultancy.
Collaborative, Research‑Led Environment: You’ll be taught by experts in environmental mathematics and renewable engineering, with opportunities to participate in live research discussions, seminars, and interdisciplinary working groups — all within the Graduate School of Environment & Sustainability.
Peer Learning & Networking: Studying alongside other MSc students in sustainability, data science, and engineering creates rich opportunities for teamwork, project exchanges, and exposure to diverse perspectives

Progression & Future Opportunities

Graduate outcomes summary: Graduates from the MSc Applied Data Science (Renewable Energy) at University of Exeter are well‑positioned to launch careers that combine data science, artificial intelligence and renewable energy expertise fields with strong global demand as clean technology grows. Common roles include Data Scientist in Energy Tech, Renewable Energy Analyst, AI/ML Specialist for sustainability, and Environmental Data Consultant:

Progression & Future Opportunities:

University services to support careers: Exeter’s dedicated Career Zone offers personalised guidance, industry networking, CV/interview preparation and workshops tailored to postgraduate STEM students, helping you connect directly with employers and opportunities in data and energy sectors.
Employment potential: The UK’s data analytics sector is predicted to grow by over 170% in the next five years, and strong investment in renewable energy technologies points to expanding roles for graduates who can apply data science to big sustainability challenges.
Industry‑relevant training & partnerships: The programme is developed with internationally recognised researchers in mathematics and renewable engineering, using state‑of‑the‑art facilities and practical activities to build real‑world skills that employers in energy tech, green consulting and AI sectors value. Long‑term accreditation value: Completing this MSc gives you a strong interdisciplinary foundation in modern data science, AI, and renewable energy systems, skills that are increasingly sought across sectors—from tech start‑ups to major energy and sustainability organisations.
Graduation outcomes: Alumni from Exeter’s data science and applied mathematics pathways go into roles where they analyse complex datasets, apply modelling to environmental systems and drive innovation in clean energy and sustainability fields.

Further Academic Progression:
Graduates can build on this MSc by progressing to PhD‑level research in areas like renewable energy systems modelling, sustainable AI, climate data science or environmental analytics, ideally suited if you’re inclined toward research or academic careers. Alternatively, many also pursue professional certifications in data science, AI or renewable energy management to enhance industry credentials and long‑term leadership opportunities.