Doing Economics: Measuring Climate Change Course

Editorial Take

The University of London’s 'Doing Economics: Measuring Climate Change' course stands out as a socially conscious introduction to data analysis, tailored for learners who want to understand climate change through an economic and policy lens. Rather than diving into complex algorithms or coding, it leverages Excel—a widely accessible tool—to demystify real-world climate datasets. This approach makes data analysis approachable for beginners while maintaining relevance to urgent global challenges.

Standout Strengths

• Real-World Data Application: Learners work with authentic climate datasets, including temperature records and extreme weather events, fostering practical skills. This hands-on experience helps bridge the gap between theory and real policy analysis.
• Policy-Relevant Curriculum: The course consistently ties data findings to socioeconomic impacts and government responses. This context helps learners see how data informs decisions on carbon pricing, adaptation, and international cooperation.
• Beginner-Friendly Excel Focus: By using Excel, the course lowers the entry barrier for non-technical learners. Step-by-step exercises build confidence in data manipulation, cleaning, and visualization without requiring coding knowledge.
• Structured Weekly Progression: Each module builds on the last, starting with data basics and culminating in policy evaluation. This scaffolding supports steady skill development and prevents cognitive overload for new learners.
• Integrated Readings and Context: Background materials enrich data exercises with historical and scientific context, helping learners interpret trends accurately. This interdisciplinary blend strengthens analytical depth and critical thinking.
• Global Relevance and Accessibility: Hosted on Coursera, the course is accessible worldwide and addresses a universally critical issue. Its focus on measurable impacts makes it valuable across diverse geographic and economic contexts.

Honest Limitations

• Excel-Centric Approach Limits Scalability: While Excel is user-friendly, it lacks the power and flexibility of programming tools like Python or R. Advanced learners may find the methods too basic for large or complex datasets.
• Shallow Technical Depth: The course prioritizes accessibility over technical rigor, which may leave learners wanting more advanced statistical or modeling techniques. It’s a starting point, not a comprehensive data science training.
• Limited Feedback in Audit Mode: Without paid enrollment, learners miss graded assignments and personalized feedback, reducing accountability and learning reinforcement for self-directed students.
• Narrow Scope Beyond Climate: The focus is strictly on climate-related data, which limits transferability to other domains. Learners seeking broad data analytics skills may need supplementary resources.

How to Get the Most Out of It

• Study cadence: Dedicate 3–4 hours weekly to complete exercises and readings on time. Consistency ensures steady progress and better retention of data techniques.
• Parallel project: Apply skills to local climate data, such as regional temperature or rainfall records. This deepens engagement and reinforces learning through personal relevance.
• Note-taking: Document each Excel function and its purpose. Building a personal reference guide aids future data tasks and skill retention.
• Community: Join course forums to discuss findings and interpretations. Peer interaction enhances understanding and exposes learners to diverse perspectives on climate issues.
• Practice: Re-analyze datasets with different visualizations or summary statistics. Experimentation builds confidence and reveals new insights from the same data.
• Consistency: Complete modules in sequence without skipping. The cumulative design means later concepts depend on earlier Excel and analytical foundations.

Supplementary Resources

• Book: 'The Climate Casino' by William Nordhaus complements the course with deeper insights into climate economics and policy trade-offs.
• Tool: Practice advanced visualizations using free tools like DataWrapper or Flourish to extend beyond Excel’s capabilities.
• Follow-up: Enroll in data science or environmental economics specializations to build on the foundational skills gained.
• Reference: IPCC reports provide authoritative data and projections that align with course themes and deepen policy understanding.

Common Pitfalls

• Pitfall: Skipping readings to focus only on Excel tasks. This misses critical context needed to interpret data meaningfully and draw valid policy conclusions.
• Pitfall: Treating Excel as limiting rather than enabling. Beginners should embrace its simplicity as a stepping stone to more complex tools later.
• Pitfall: Underestimating data cleaning time. Real-world datasets often require significant preparation, so allocate extra time for preprocessing steps.

Time & Money ROI

• Time: At 8 weeks and 3–4 hours per week, the time investment is manageable for working professionals and students alike.
• Cost-to-value: While the course is free to audit, paying for the certificate adds value through structured feedback and credentialing for resumes.
• Certificate: The verified certificate enhances credibility for roles in sustainability, public policy, or environmental advocacy.
• Alternative: Free climate data courses exist, but few integrate Excel practice with policy context as effectively as this offering.

Editorial Verdict

This course fills a unique niche by combining accessible data analysis with urgent global challenges. It doesn’t aim to produce data scientists but rather informed citizens and policy-ready professionals who can interpret and communicate climate data effectively. The use of Excel ensures broad accessibility, and the structured progression supports learners with little prior experience. For those passionate about climate change and eager to contribute meaningfully through data, this course offers a practical and motivating entry point.

We recommend it particularly for students in social sciences, public policy, or environmental studies who need to strengthen their quantitative skills without diving into programming. While it won’t replace advanced analytics training, it builds confidence and competence in working with real data—a crucial first step. Pair it with supplementary reading and practice, and it becomes a solid foundation for further learning and impactful work in climate economics and policy analysis.