MSc Environmental Data Science and Analytics

Year 1

(Award available for year: Master of Science)

Learning outcomes

On successful completion of the programme, the student will be able to:
1. Critically analyse, interpret, and visualise complex environmental data to generate insights for addressing real world environmental challenges.
2. Demonstrate proficiency in applying data science techniques to a variety of environmental contexts.
3. Interpret and understand large, hyperdimensional environmental data sets, understanding the intricacies of data sources, analysis processes, and outcomes.
4. Formulate creative, data-driven solutions and insights for complex environmental issues, informed by an understanding of the environmental science context.
5. Understand and apply appropriate technical skills to collect data.

Skills Learning Outcomes
On successful completion of the programme, the student will be able to:
1. Apply data science and analytics techniques to solve practical, real-world/industry problems, transitioning from fundamental knowledge to creative application.
2. Engage in collaborative problem-solving, working in groups to apply technical skills and derive novel solutions.
3. Effectively communicate complex data insights to various audiences through various formats, including policy briefs, blogs, and annotated code.
4.Apply critical thinking skills towards sophisticated insight generation and informed decision-making in multiple contexts.
5. Conduct in-depth research projects, including demonstrating project management skills and the ability to integrate data science techniques with varying types of data.

Assessment

This programme’s assessment strategy considers diversity and inclusivity, ensuring that assessments cater to a wide range of learning styles and backgrounds. Assessments include a focus on real-world applications, employing practical problems and datasets to enhance learning. This approach is key in this programme, being geared towards practical skills in environmental data science. The assessment mix is a balance of practical application, with assignments that include project-based tasks, case studies, and data analysis projects, reflecting the hands-on nature of the field. The programme also aims to balance individual and group projects to develop teamwork skills, crucial in terms of future employment. Technology has been integrated into assessments, including use of modern analytical techniques through coding, ensuring students gain experience with approaches commonly used in academia and by employers. Individual modules will provide timely, constructive feedback, which is essential for iterative learning and skill development in data science, as students build on both contextual knowledge and mastery in data science techniques.
Individual modules provide timely, constructive feedback, which is essential for iterative learning and skill development in data science, as students build on both contextual knowledge and mastery in data science techniques. A blend of assessment methods, such as project work and presentations, are used for monitoring ongoing progress and offering regular feedback.
Collectively, the assessment approach is designed to foster skills development beyond the traditional academic skills (technical expertise and research), by supporting students’ competency in collaboration and teamwork, critical thinking, and communication, thereby aligning with the objective of preparing students for professional roles in data science. This comprehensive approach aims to create a robust and effective assessment strategy, blending the University's overarching assessment principles with the specific needs of the Environmental Data Science and Analytics program.