Data Science for Environmental Sustainability (MSc)

How will I be taught?

The programme is taught by internationally recognized academics who are experts in the fields of environmental science and data analytics. Our teaching approach is informed by the latest research and is aligned with current industry practices, ensuring that the curriculum is both academically rigorous and relevant to professional demands. Faculty members integrate their research and industry experience into the curriculum, offering firsthand insights into the latest advancements in data science and environmental sustainability.

The curriculum combines theoretical knowledge with practical application. Lectures, supported by practical sessions, give you a strong foundation in both theory and practice, while seminars, workshops, and tutorials help you apply what you’ve learned. These interactive formats support hands-on projects and in-depth discussions, so that you can tackle real-world environmental challenges and enhance your professional readiness.

Research integration is a key component. You’ll engage with up-to-date data, relevant case studies, and the newest research findings, connecting academic theory with practical environmental issues. Guest lecturers and industry experts will bring practical perspectives to your learning experience.

Hands on learning is a focal point of the programme, giving you the chance to develop practical skills in realistic contexts. Through team-based projects and individual research, you’ll apply sophisticated data science techniques to complex environmental sustainability problems.

How will I be assessed?

Assessments vary by module and include coursework, portfolio projects, written assessments, class tests, oral and poster presentations, and practical projects. Each module employs specific types of assessments to effectively evaluate both theoretical knowledge and practical skills.

Informal assessments are used throughout the MSc to enhance learning to ensure your grasp of foundational concepts. These assessments are brief and frequent, serving as building blocks that prepare you for more comprehensive summative assessments. Summative assessments are formal evaluations that directly contribute to your degree.

How will I be supported?

We offer a comprehensive support structure designed to enhance the student experience throughout your academic journey.

At the beginning of the programme, you will be assigned a personal tutor who will be your main point of contact, offering guidance on both academic and personal matters in a confidential and supportive manner. They are committed to helping you navigate through your studies and any challenges you may encounter.

Feedback is a fundamental element of our educational strategy. Throughout the course, you will have numerous opportunities for both receiving and participating in feedback during contact sessions. Activities such as peer assessments, and self-assessments are integral to this process. Additionally, you will receive constructive feedback from our teaching staff, as well as in certain modules, from external stakeholders, providing insights into the kind of feedback prevalent in professional environments.

Learning Central is an online learning resource where you can access a wide array of learning materials including lecture recordings, lecture handouts, and extensive bibliographic resources. This platform also supports interactive discussions and electronic exercises. Beyond digital resources, you have full access to the computing facilities in both the School of Earth and Environmental Sciences and the School of Computer Science and Informatics, along with dedicated study spaces designed to support all aspects of your academic work.

Beyond academic support, you’ll have access to a range of school-specific support services. This may include study skills workshops, career guidance sessions, mental health and well-being support, and disability services. These services are designed to ensure that you feel supported and empowered to succeed.

We are committed to creating an inclusive, supportive, and welcoming environment for all students and staff, regardless of age, ethnicity, disability, gender, nationality, sexual orientation, religion, or socio-economic background. Our programme proudly supports the Athena SWAN principles, and we celebrate our bronze status, reflecting our dedication to advancing gender equality and diversity within higher education.

What skills will I practise and develop?

The Learning Outcomes for this Programme describe what you will achieve by the end of your programme at Cardiff University and identify the knowledge and skills that you will develop. They will also help you to understand what is expected of you.

On successful completion of your Programme you will be able to:

Knowledge & Understanding:

• Describe and analyse complex environmental challenges, including climate and weather extremes, and understand their societal impacts through data-driven approaches.
• Demonstrate a systematic understanding of advanced data analytics techniques, including remote sensing, GIS, and big data, and their application to environmental science.
• Understand the relevance of data science to global development frameworks and multilateral agreements, particularly in promoting environmental sustainability.
• Exhibit a comprehensive understanding of AI and machine learning techniques and their applications in environmental data analysis and problem-solving.
• Display knowledge of current industry practices and emerging trends in environmental data science, preparing for careers in the environmental sector

Intellectual Skills:

• Critically analyse and evaluate environmental data, methodologies, and research to inform decision-making and problem-solving in sustainability contexts.
• Apply innovative and analytical thinking to solve complex environmental problems using data science and AI techniques and develop effective sustainability strategies.
• Design and conduct independent research using advanced data science methods, including programming, computational modelling, and machine learning.
• Demonstrate the ability to interpret and manage uncertainty in environmental data and models, making well-reasoned conclusions based on statistical and computational analysis.
• Synthesize knowledge from environmental science and data science disciplines to address multifaceted environmental challenges.

Professional Practical Skills:

• Plan, develop, and manage projects that leverage data science and AI to promote sustainability, including implementing monitoring and evaluation strategies to ensure effectiveness.
• Demonstrate technical proficiency in using industry-standard data science and AI tools for environmental data collection, management, and analysis.
• Conduct thorough analysis and create visualizations of environmental data, using both qualitative and quantitative methods to inform management and policy.
•  Plan, conduct, and present a comprehensive dissertation project, applying advanced data science methodologies to address a specific environmental sustainability challenge.
• Develop the ability to communicate complex technical information and data-driven insights clearly and effectively to both specialist and non-specialist audiences.

Transferable/Key Skills:

• Create and present effective data visualizations, including static, animated, and interactive formats, to communicate environmental data insights.
• Work collaboratively in multidisciplinary teams, building partnerships to advance data-driven environmental sustainability initiatives.
• Engage in continuous professional development, reflecting on personal performance and staying updated with advancements in data science, AI, and environmental sustainability.
• Develop strong analytical and critical thinking skills, enabling the evaluation and synthesis of information from diverse data sources to support evidence-based decision-making.
• Communicate effectively in various formats, both written and oral, to convey complex data science and environmental information and advocate for sustainable practices.