Course:FRE521D
| FRE 521D: Data Analytics in Climate, food and Environment | |
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| FRE 521D | |
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| Instructor: | Asif Neloy |
| Email: | TBD |
| Office: | TBA |
| Office Hours: | TBA |
| Class Schedule: | Jan 5 to Feb 13, 2026
Mondays/ Wednesdays 12:30 - 2:00 PM |
| Classroom: | MCML 154 |
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| Syllabus | |
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COURSE DESCRIPTION
This applied course develops practical skills to design and run end-to-end data pipelines for the climate, food, and environment sectors. Students will source data from files, APIs, and databases; use SQL for reliable access; build reproducible Extract, Transform, and Load (ETL) in Python; and create refreshable outputs with clear visual explanations for stakeholders. Cases include using weather and wildfire data for climate risk and logistics timing, food CPI and commodity prices for procurement and pricing decisions, and ESG ratings and water quality metrics for screening and compliance.
Beyond the technical skill-building, this course is designed to help you think and work like an applied data analyst in the climate, food, and environmental sectors. You will learn how to move from a real decision question to a clean, reproducible data pipeline that produces trustworthy insights for stakeholders. By the end of the course, you will know how to find and access the right data, build efficient SQL and Python workflows, validate and document your work for handover, and present visual stories that inform decisions for producers, retailers, investors, NGOs, or policymakers.
LEARNING OUTCOMES
SQL & Data Access
- Understand database schemas, keys, and relationships to identify correct join paths
- Write efficient SQL queries using joins, CTEs, window functions, and pivot/unpivot
- Create analysis-ready table views that feed directly into downstream analytics
ETL with Files and APIs
- Build ETL workflows that pull data from CSV/JSON files and API endpoints
- Handle authentication, parameters, pagination, and rate limits for reliable API access
- Store raw and cleaned data separately and document assumptions for reproducibility
Data Wrangling & Engineering Foundations
- Use Python to merge, reshape, and manage data types across multiple sources
- Apply validation checks for ranges, missingness, and key integrity
- Maintain clear data lineage notes linking inputs to outputs for auditability
Analysis & Visualization for Decision Support
- Frame a clear decision question and choose appropriate measures and groupings
- Run descriptive summaries and trend checks to surface insights
- Produce reproducible tables and visualizations that address stakeholder questions in climate, food, and ESG contexts
REAL-WORLD APPLICATIONS IN CLIMATE, FOOD & ENVIRONMENT
- How do we turn raw climate, food, and ESG data into refreshable analyses that support real decisions by producers, retailers, lenders, or policymakers?
- Which SQL patterns and access contracts make weekly or daily updates reliable, and how do we extend them with API pulls without breaking provenance?
- What story should our visuals tell about seasonality, regional anomalies, or risk flags, and how do we show uncertainty responsibly?
ASSESSMENT METHODS
| Activity | % of Grade |
|---|---|
| Assignments / Labs | 20% |
| Project | 30% |
| Quizz | 10% |
| Participation | 10% |
| Midterm | 30% |
| Total | 100% |