CSOL-208: Data Science for Climate Solutions
CSOL-208: Data Science for Climate Solutions
Fall 2026 • 2 Units
schedule Wednesday, 12:00pm - 1:49pm
location_on 311 Wellman Hall
This course integrates data science fundamentals into the climate solutions curriculum, reframing them for an AI-driven era. Students practice data organization, visualization, and quantitative analysis by communicating in natural language with AI systems. We explore modern code-developemnt clients, MCP tooling, LLM API use, local models, and development of AI agents. Alongside hands-on, team based projects, we also critically examine AI's reliability, ethics, and environmental impacts.
This course is part of the Master of Climate Solutions program at UC Berkeley, a professional degree designed to train the next generation of climate leaders. The MCS program combines rigorous technical training with practical skills in policy, finance, and leadership to address the climate crisis. Students interested in the program can learn more and apply at climatesolutions.berkeley.edu.
Instructors
Carl Boettiger
Associate Professor, ESPM
Carl works on ecological forecasting and decision making under uncertainty, focusing on regime shifts and data science approaches to environmental problems. Co-founder of rOpenSci and the Schmidt Center for Data Science & Environment at UC Berkeley.
[email protected] WebsiteOffice Hours
Fridays, 12:00 PM
Gaby May Lagunes
Graduate Student Instructor (she/her)
Gaby is a PhD student in ESPM at UC Berkeley, working on climate models, hydrology, and data science. She holds a master's in Data Science and Information from the Berkeley I School and brings 5+ years of industry experience across a range of data roles, along with experience as a D-Lab consultant.
[email protected] D-Lab ProfileOffice Hours
Mondays, 4:00–5:00 PM
14 Weeks
One session per week, ~2 hours each
Team-Based
Hands-on collaborative projects
MCS Core
Required for Master of Climate Solutions
The Agent-First Methodology
We do not memorize syntax. Instead, we learn to:
Architect
Design data flows
Prompt
Direct AI implementation
Audit
Verify outputs
Deploy
Ship solutions
Four Core Modules
14 sessions over 14 weeks
Module 1
The AI-Data Analyst
Sessions 1-4
Build an interactive emissions dashboard. Learn AI-assisted coding, data cleaning, visualization, and database querying.
Module 2
Spatial Data & Environmental Justice
Sessions 5-7
Map data center environmental impacts. Analyze geospatial patterns, demographic data, and biodiversity impacts through an environmental justice lens.
Module 3
Working with LLMs & Unstructured Data
Sessions 8-10
Extract structured data from sustainability reports. Work with LLM APIs programmatically, structured outputs, and Model Context Protocol.
Module 4
The Capstone Studio
Sessions 11-14
Build a deployable MVP. Scope projects, sprint development, refine user experience, and present live demos.
Core Tech Stack
Industry-standard tools for rapid prototyping
VS Code / Kilo Code
AI-augmented IDE and coding agent
HTML / CSS / JS
Static web apps & dashboards
DuckDB
Data & spatial queries
LangChain
Document parsing
Questions?
For more information about this course, please reach out to the instructor.