Climate Data Science is an interdisciplinary field that combines economics, environmental science, and advanced data analysis to understand and address the challenges of climate change. This course is designed to provide learners with practical skills in analysing climate data, interpreting economic models, and applying quantitative techniques to real-world environmental problems.

Throughout this course, learners will explore key concepts such as marginal abatement cost theory and its role in evaluating climate mitigation strategies. You will learn how to construct and interpret marginal abatement cost curves using Python, gaining hands-on experience in data modelling and economic analysis.

The course also introduces important economic perspectives related to climate change, including immigration dynamics and policy implications. Learners will develop an understanding of how economic theory connects with environmental decision-making and global climate systems.

A major focus of the programme is emissions and energy data analysis. You will work with real-world datasets to examine emissions intensity across regions, interpret long-term environmental trends, and understand the relationship between energy systems and carbon output. These skills are essential for evidence-based climate policy analysis and sustainability research.

In addition, the course applies data science techniques to regional case studies, particularly in South Asia. Learners will analyse electricity access patterns and energy generation mixes, helping them understand the challenges and opportunities in developing economies.

By completing this course, learners will gain a strong foundation in data science applications for climate economics, including Python-based modelling, statistical interpretation, and policy-oriented analysis. These skills are highly valuable for careers in environmental consulting, sustainability analytics, research, and policy development.