MSc in Data Science and Artificial Intelligence for Sustainability, Cranfield University

12 Months

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About this course

This MSc program focuses on applying digital tools and models, such as Artificial Intelligence, to solve global sustainability issues related to energy production and environmental management. The course aims to equip students with multidisciplinary skills to analyze data from various energy systems, develop control strategies, and support essential objectives like reaching Net Zero emissions. The program is suitable for graduates from engineering, computer science, mathematics, environmental, and IT backgrounds who wish to pursue technical management careers in digital transformation for sustainability. It offers both full-time and part-time study options, starting in October, with a combination of taught modules, group projects, and an individual research project all delivered at Cranfield campus.

Why this course is highly recommended

This program is industry-informed, with extensive engagement from leading energy companies, ensuring that the curriculum remains relevant and practical. It offers access to state-of-the-art facilities and real-world projects, including industry collaborations. The course develops vital skills in data acquisition, AI algorithms, and simulation platforms that are critical in the rapidly growing field of digital sustainability, making graduates highly desirable for roles in industry, government, and research. Additionally, the opportunity to learn from thought leaders and industry experts provides a cutting-edge educational experience.

Specialisation

This course specializes in the digital transformation of sustainability through data science and AI, with a focus on energy systems and environmental assessment. It combines modules on geospatial data management, decision science, Python programming, data analytics, AI applications in energy, and environmental impact assessment, preparing students to address complex challenges like energy security, decarbonization, and sustainable development.

Course fees

Application fees

1st year tuition fees

27.40L

Living cost

Degree requirements

Applicants typically need a UK lower second-class (2:2) honours degree or equivalent in a related science or engineering discipline. International qualifications are assessed on an individual basis. Those who do not meet the standard requirements may apply for a Pre-master's course, which, upon successful completion, qualifies them for admission to this MSc. The course also requires ATAS clearance for international students and proof of effective English communication skills.

English language test

DUOLINGO

120

IELTS

6.5

TOEFL

PTE

Want to learn more about the admission process, eligibility criteria, and acceptance rates for international students? Visit the Cranfield University admission page for complete details.

Career prospects

Graduates of this course can pursue diverse roles in the energy industry, government, or research sectors. Examples include Energy Analyst in data science, Offshore Energy Analyst, and Energy and Sustainability Analyst. The program’s industry links and career support services, like CV development and job fairs, help students secure promising positions worldwide. The skills gained in data analysis, AI, and environmental assessment open pathways to leading roles in energy management, sustainability consulting, and policy development.

FAQs

What is the duration of the MSc program?

The course lasts one year if studied full-time, or two to three years if pursued part-time.

What are the main modules covered?

Key modules include GIS and Spatial Data Management, Decision Science, Scientific Python, Data Analytics for Sustainability, Artificial Intelligence for Energy Systems, and Sustainability and Environmental Assessment.

What are the English language requirements?

Applicants need to demonstrate effective communication skills in English, with accepted tests such as IELTS (minimum 6.5 overall, 5.5 in each skill) or TOEFL (minimum 92 overall, with specific minimums in each component). Further details are available in the course guide.